Working Paper Series

U.S. Environmental Protection Agency National Center for Environmental Economics 1200 Pennsylvania Avenue, NW (MC 1809) Washington, DC 20460 http://www.epa.gov/economics

Moving Forward with Incorporating “Catastrophic”

Climate Change into Policy Analysis

Elizabeth Kopits, Alex L. Marten, Ann Wolverton

Working Paper # 13-01

January, 2013

NCEE Working Paper Series Working Paper # 13-01

January, 2013

DISCLAIMER The views expressed in this paper are those of the author(s) and do not necessarily represent those of the U.S. Environmental Protection Agency. In addition, although the research described in this paper may have been funded entirely or in part by the U.S. Environmental Protection Agency, it has not been subjected to the Agency's required peer and policy review. No official Agency endorsement should be inferred.

Moving Forward with Incorporating “Catastrophic” Climate Change into Policy Analysis

Elizabeth Kopits, Alex L. Marten, Ann Wolverton

1

Moving Forward with Incorporating “Catastrophic” Climate Change into Policy Analysis

Elizabeth Kopits, Alex L. Marten, Ann Wolverton1

Abstract

It has often been stated that current studies aimed at understanding the magnitude of optimal climate

policy fail to adequately capture the potential for “catastrophic” impacts of climate change. While

economic modeling exercises to date do provide evidence that potential climate catastrophes might

significantly influence the optimal path of abatement, there is a need to move beyond experiments

which are abstracted from important details of the climate problem in order to substantively inform the

policy debate.

This paper provides a foundation for improving the economic modeling of potential large scale impacts

of climate change in order to understand their influence on estimates of socially efficient climate policy.

We begin by considering how the term “catastrophic impacts” has been used in the scientific literature

to describe changes in the climate system and carefully review the characteristics of the events that

have been discussed in this context. We contrast those findings with a review of the way in which the

economic literature has modeled the potential economic and human welfare impacts of events of this

nature. We find that the uniform way in which the economic literature has typically modeled such

impacts along with the failure to understand differences in the end points and timescales examined by

the natural science literature has resulted in the modeling of events that do not resemble those of

concern. Based on this finding and our review of the scientific literature we provide a path forward for

better incorporating these events into integrated assessment modeling, identifying areas where

modeling could be improved even within current modeling frameworks and others where additional

work is needed.

Keywords: Climate Change, Catastrophes, Integrated Assessment Model

JEL Codes: Q54, Q58

1 National Center for Environmental Economics, U.S. Environmental Protection Agency, Washington, DC 20460. Corresponding Email: Kopits.elizabeth@epa.gov . The views expressed in this paper are those of the authors and do not necessarily reflect the view or policies of the U.S. Environmental Protection Agency. The authors appreciate the helpful comments of Tim Lenton of the University of Exeter and Steve Newbold of the U.S. EPA National Center for Environmental Economics.

2

1. Introduction

It is common within the academic and public discourse on climate change for the term catastrophe to be

invoked when describing the possible outcomes of a changing climate and in justifying particular

responses to the problem. In fact it has been suggested that the potential for “catastrophic impacts” as

a result of climate change is the most important aspect of the problem for determining the optimal level

of response (Pindyck and Wang 2012, Weitzman 2009). Pindyck (2012) goes so far as to argue that “the

economic case for a stringent GHG abatement policy, if it is to be made at all, must be based on the

possibility of a catastrophic outcome.” Thus, it is perhaps not surprising that analyses of greenhouse gas

mitigation benefits are often criticized for failing to adequately capture possible catastrophic impacts

(e.g., Tol 2009, NAS 2010). Even the U.S. government in its primary work to value the benefits of

greenhouse gas abatement notes a lack of accounting for catastrophic impacts as a major caveat that

requires their analysis only be considered “provisional” (U.S. Interagency Working Group on Social Cost

of Carbon, 2010). However, despite the seeming importance of such potential climate change related

events there has been little progress in defensibly integrating catastrophic impacts into analyses

considering the benefits of climate policy.

One obstacle that has impeded forward progress on this front is the inconsistent and sometimes

nebulous way in which the expression “catastrophic impacts” has been used (Hulme, 2003). The term

has been adopted as a catch-all phrase that refers to any climate induced impact that exhibits one or

more of a number of characteristics: relatively sudden occurrence, irreversible transition to a new state

after crossing a threshold, relatively large physical or economic impacts, or relatively low probability but

extensive impacts. For this reason the types of impacts covered under the catastrophic moniker are

numerous and heterogeneous. For example, the term climate catastrophe has been used to describe

everything from dieback of Amazon rainforests over the coming decades to the potential massive

release of methane emissions from the sea floor over the next thousand years (Lenton et al. 2008).

Some even have argued for establishing an overall global threshold for climate change, below which we

are deemed safe from violating "non-negotiable planetary preconditions… [and] avoid the risk of

deleterious or even catastrophic environmental change at continental to global scales” (Rockstrom et al.

2009). The authors acknowledge that determining what is safe is a normative judgment, but link it to the

notion that deleterious or catastrophic effects from climate change would occur when Earth systems are

pushed out of the Holocene state (a period of relatively stability over the past 10,000 years) (Rockstrom

et al. 2009). The ambition of an overall global warming threshold was formally endorsed in the 2009

3

Copenhagen Accord, in which more than two dozen key countries -- representing more than 80 percent

of the world's global warming pollution – agreed to register non-binding national commitments to

combat climate change:

“To achieve the ultimate objective of the Convention to stabilize greenhouse gas concentration in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system, we shall, recognizing the scientific view that the increase in global temperature should be below 2 degrees Celsius, on the basis of equity and in the context of sustainable development, enhance our long-term cooperative action to combat climate change” (UNFCCC 2009).

In public discourse catastrophic impacts are often invoked as a seemingly monolithic occurrence2, a

tendency that is also often present in the economic analyses of optimal climate policy conditional on the

potential for such events. By assuming uniformity across the multitude of characteristics over which

these potential climate “catastrophes” may vary, the economic research on the subject has severely

limited its ability to substantively inform policy discussions. In addition, many economic modeling efforts

fall substantially short when it comes to incorporating scientific evidence regarding the causes,

likelihood, and potential physical impacts of such climate change induced events. The former may arise

from an absence of literature that summarizes the significant differences between potential large scale

events resulting from climate change and what that means for incorporating them into economic

analysis, while the latter appears to be the result of fundamental differences between disciplines as to

what constitutes relatively rapid or large changes and the appropriate end points to measure in policy

analysis. Both of these concerns have been observed by natural scientists (e.g., Hulme 2003), and calls

are increasing across the scientific community for more research on welfare impacts, with better links to

the scientific evidence on how physical processes are likely to unfold (e.g., Lenton 2011, Lenton and

Ciscar 2012).

In this paper we seek to provide a foundation to help improve the economic modeling of potential large

scale impacts of climate change within Earth systems in order to understand their influence on

estimates of socially efficient climate policy. We begin by considering how the term “catastrophic

impacts” has been used in the scientific literature to describe changes in the climate system and

2 Examples of such statements include: “We have a window of only 10-15 years to take the steps we need to avoid crossing catastrophic tipping points” (Jan Peter Balkenende & Tony Blair, October 20, 2006), “Until now, leaders have focused on slowing warming to 2 degrees Celsius to prevent catastrophic changes associated with climate change” (MIT News, June 14, 2012), “Even if the ultimate result were an Earth that is still hospitable to mankind, the transition could be catastrophic” (The Economist, June 18, 2012).

4

carefully review the characteristics of the events that have been discussed in this context. We explore

the potential economic and human welfare impacts of such events and contrast those findings with a

review of the way in which the economic literature has modeled these events classified as possible

climate catastrophes.3 We find that the relatively uniform way in which the economic literature has

typically modeled such impacts along with the failure to understand differences in the end points and

timescales examined by the natural science literature have resulted in the modeling of events that do

not resemble those of concern in reality. Based on this finding and our review of the scientific literature

we suggest a path forward for better incorporating these events into integrated assessment modeling,

identifying areas where modeling could be improved even within current IAM frameworks and others

where additional work is needed.

2. Catastrophic Impacts from the Scientific Perspective

An often cited technical definition for the term catastrophe is “when the climate system is forced to

cross some threshold, triggering a transition to a new state at a rate determined by the climate system

itself and faster than the cause” (NRC 2002). This characterization captures two of three salient aspects

of the typical use of the term catastrophe in the scientific literature. First, the event occurs relatively

quickly. Second, it causes a natural system to move to a new steady state. Catastrophes related to

climate change have also been termed “surprises” in the scientific literature, which the IPCC (1996)

defines as the rapid, non-linear response of a natural system to anthropogenic forcing.4 This definition

highlights a third important aspect of the term catastrophe: it could potentially result in a relatively

large impact. In particular, the potential for relatively abrupt shifts in the states of natural systems are a

cause for concern due to the “large and widespread consequences” that may result (IPCC 2007) and the

possibility that they occur so rapidly that “human and other natural systems have difficulty adapting”

(NRC 2002; Posner 2004).

3 In this paper we focus on the economic study on specific large climate induced Earth system, outside of direct temperature response to anthropogenic emissions. Alternatively, there exists an economic literature that has focused on the policy implications of potentially large welfare impacts associated with a significantly stronger than expected climate response to anthropogenic emissions (e.g., Weitzman, 2011). 4 The IPCC has also previously used the potentially confusing terminology of “large scale discontinuities” to describe such events. However, we note that the notion of a discontinuity in this case would arise from observing the time path of the system over a long time horizon, and does not refer to a mathematical discontinuity in the state transition dynamics of the system.

5

Threshold or tipping point behavior

Climate-change related events described in this manner are often associated with crossing a threshold,

or “tipping point,” in the Earth system. For instance, Perrings (2003) suggests that abrupt climate change

due to a relatively small change in forcing is the result of triggering a sudden switch from one stable

state to another. In this context Schneider (2003) also notes “anomalies can push the… system from one

equilibrium to another.” Schlesinger et al. (2007) defines a climate threshold as a point at which a

relatively small perturbation in radiative forcing can result in a large, sudden change in the climate

system. Kriegler et al (2009) defines a tipping point as one where a large-scale change or discontinuity in

the Earth system will occur due to a small change in global mean temperature. Such an abrupt transition

of an Earth system from one equilibrium state to another could easily be envisioned as a catastrophic

change for that particular system.

Many natural systems exhibit this type of tipping or threshold behavior.5 For instance, Sheffer et al.

(2001) note that a shallow lake with rich vegetation could abruptly change from clear to turbid water

(i.e. due to algae bloom) in reaction to increased nutrient loadings. When this occurs, vegetation dies off

and the diversity of lake life declines. Alley et al. (2003) use the analogy of a canoe to describe this

behavior: A paddler that leans over slightly in a canoe experiences only a small tilt, but if the paddler

leans over a bit more the canoe may suddenly roll over, dumping the surprised paddler into the lake.

What is common across these abrupt changes in state is that it typically consists of three basic

components (NRC 2002): a trigger – in the case of the lake, added nutrients and in the case of the canoe,

leaning over; an amplifier – the mechanism through which a small change in the lake or canoe causes a

much larger result; and a source of persistence – fish reinforce the turbidity in the lake, while basic

physics ensure it is more difficult to flip the canoe back over – making the new state stable and self –

reinforcing.

We can similarly characterize many of the Earth systems affected by climate change using these three

basic components of threshold or tipping point behavior: trigger, amplifier, and persistence. The

National Academy of Science (NRC 2002; Alley et al. 2003) and the Intergovernmental Panel on Climate

Change (IPCC 2007a) among many others have suggested that a rise in global mean temperature due to

5 See Sheffer et al. (2001) for examples of other abrupt ecosystem shifts discussed in the literature. Holling (1973) and May (1977) are two early papers that discuss this phenomenon.

6

increases in the atmospheric concentration of greenhouse gases from the burning of fossil fuels,

deforestation, and other land-use change could trigger changes within an Earth system. Feedback

effects within these systems could amplify these changes (e.g., surface melting of an ice sheet can affect

the speed of ice flow) leading to even larger impacts, such as the complete collapse of ice sheets,

substantial dieback of the Amazon forest, or the thawing of permafrost, to name a few. Finally, the new

state may exhibit persistence: the Earth system is described as eventually settling into a new but

fundamentally different stable state that is irreversible (e.g. NRC 2002; IPCC 2007) or reversible only

over very long time scales (Perrings 2003; Schneider 2003). Our analogy with other natural systems ends

here, however. While a lake ecosystem or canoe has a defined and limited set of boundaries that

constrains the problem, climate change affects the entire Earth through the coupled system containing

the atmosphere, oceans, ice, and biological systems, which increases the analytical challenge associated

with understanding the overall impacts of crossing of a given threshold within a particular system.

While much of the focus with regard to climate change has been on events that result from the crossing

of a potential threshold in a natural system that leads to a new equilibrium (referred to as bifurcation),

Lenton et al. (2008) argue that it is important to consider a broader set of tipping elements in the

climate system. They define the term ‘‘tipping element’’ to describe "subsystems of the Earth system

that are at least sub-continental in scale and can be switched—under certain circumstances—into a

qualitatively different state by small perturbations. The tipping point is the corresponding critical

point—in forcing and a feature of the system—at which the future state of the system is qualitatively

altered." This characterization would include the typical bifurcation point discussed above along with

cases where the system may potentially bounce between states after the threshold is crossed.

Therefore, the transition could be irreversible or a phase after which the system returns to its prior

state. Lenton et al. (2008) stress that even though some transitions are reversible in principle, they are

unlikely to be reversed in practice for many centuries because of the inertia in rising temperatures.

Time scales, geographic breadth, and climate end points

Scientific definitions of what can be considered a catastrophe also encompass a wide range of time

scales, geographic breadth, and climate end points. Events within the scientific literature described as

resulting in “rapid,” “sudden,” or “abrupt” state change include qualitative Earth system changes that

can range in time scale from decades (e.g., NRC 2002; Clark et. al 2002; Alley et al. 2003; USCCSP 2008),

to a few centuries (e.g., Shindell 2007), and sometimes even up to millennia (e.g., Lenton et al. 2008).

7

This variation exists because shifts in biological systems are often considered rapid in relation to the

timescale of the previous stable state. For example, the transition in the Earth’s biosphere from the last

glacial into the present interglacial condition occurred over millennia but this is still less than 5% of the

time that the previous state had lasted (Barnosky et al. 2012). The geographic scale of the event’s

impact may also be regional (e.g., Western Europe in the case of changes in the thermohaline circulation

guiding ocean currents), continental (e.g., monsoon season change in Africa), or global (e.g., methane

releases from thawing permafrost). Events that scientists classify as abrupt or sudden also vary in the

affected physical end points (e.g., temperature, precipitation, storms), and the overall impact will

depend on the interaction between all of these characteristics.

Choosing how to define a potentially catastrophic event given this variation has led to multiple methods

of ad-hoc classification. Some authors have proposed using geographic scale as the metric. For instance,

an event would qualify as a potential catastrophe when it occurs on a country or even continent-wide

basis (e.g. NRC 2002; Clark et al. 2002; Lenton et al. 2008; USCCSP 2008). Posner (2004) proposes

limiting the definition of a catastrophe to events that are truly global in scale: those that could end

advanced civilization as we know it. Others have proposed that the time scale should also be used to

classify potentially catastrophic events. Posner (2004) points out that “a span of a million years, let alone

of a billion or a trillion, belongs to a timescale that cannot have real meaning for human beings living

today.” Lenton et al. (2008) proposes a short list of “policy relevant tipping points” based on two time

scales: Earth system changes that may be triggered within this century – on the “political time horizon”

– and those that would undergo a qualitative change within this millennium – within the “ethical time

horizon.”

Uncertainty

The scientific literature also has given notable thought to how the level of uncertainty surrounding a

particular tipping point might influence its potential classification as a catastrophe. As noted by Alley et

al. (2003), there is a high degree of uncertainty inherent in attempting to identify and quantify the

causes of abrupt climate change, particularly near thresholds where the behavior of natural systems can

become unpredictable. Therefore large error bounds exist around when a catastrophic event might be

triggered, in addition to substantial uncertainty about how the transition would occur, and the ultimate

impacts associated with them (e.g. Schlesinger et al. 2007; Keller et al. 2008). From a modeling

perspective, it is difficult to capture processes that are deeply uncertain and where our understanding of

that uncertainty exists with a low level of confidence. Perrings (2003) notes that the nature of the

8

uncertainty will be inherently difficult to characterize in the case of climate change induced catastrophic

events when both the full set of possible outcomes in addition to the probability distribution of the

outcomes are largely unknown. However, some researchers have attempted to better classify and

understand the uncertainties associated with these events (see Lenton et al. (2008), Lenton (2011) for

summaries).

Another difficulty in assessing the uncertainty around tipping points is that many aspects of these events

will be path dependent such that “the same forcing might produce different responses depending on

the pathway followed by the system” (Schneider, 2003). For instance, Schlesinger et al. (2007) indicate

that even a “slow, smooth forcing can induce abrupt, persistent changes in the climate system or a

‘threshold’ response.” Shindell (2007) has noted that sudden climate change can occur due to either

“rapid changes in the forcings or from the potential for feedbacks to be strong and perhaps nonlinear.”

Numerous authors note that the forcing that could trigger a large response in the climate system may

not by itself be all that notable.6

Finally, it is worth noting that the nature of a surprise is that it is unanticipated (Schneider 2003).

Schneider argues for differentiating between abrupt events that are imaginable or expected (or at least

not unexpected) and those that are “true surprises” where the outcome is unknown. In the former case,

even with all the inherent uncertainties discussed above, we may be able to bring modeling expertise to

bear with regard to potential impacts. In the latter case, however, it may only be possible to “identify

imaginable conditions for surprise” (Schneider 2003). Noting this important caveat we proceed to a

discussion of how economists currently define and model catastrophes.

6 These uncertainties affect the ability to model and predict Earth system behavior. Overpeck and Cole (2006) note that “the biggest obstacle to reliable abrupt climate change prediction is the limited state of our coupled atmosphere-ocean and ice sheet modeling capability….A major challenge to the scientific community is to build models that can simulate the observed record of past abrupt climate change in a realistic manner.” Lenton (2009) points out that IPCC projections of climate change response do a relatively poor job of predicting abrupt or nonlinear effects of climate change because they: (1) focus on global mean quantities (i.e. regional-scale spatial variability is smoothed out); (2) use simple climate models such as MAGICC that are designed to capture some aspects of more complicated large-scale general circulation models (GCMs) but exclude their non-linear and stochastic aspects; and (3) often average GCM output over long time horizons and sometimes over a group of runs, which smoothes out short-term temporal variability.

9

3. How Economists Define and Model Climate Catastrophes

There are several differences in the way potential climate catastrophes are characterized and discussed

in economics compared to the scientific literature. An economic catastrophe is often defined with

regard to how rapidly it will occur relative to the time required for mankind to adapt to this new state of

the world. For instance, the NRC (2002) defines abrupt climate change from a societal perspective as

“having sufficient impacts to make adaptation difficult.” Likewise, Williams (2009) defines rapid climate

change as fast enough – a decade or two – that adaptation is impossible even for the richest countries.

Despite the importance of the time scale in economics Hulme (2003) notes that this area is a major

source of confusion and miscommunication between the scientific and policy communities as the term

“abrupt as used by the paleoclimate community has different meanings to abrupt as used in more

popular discourse.” In turn the economics literature has typically assumed time scales over which

impacts will become fully realized that are often much shorter than the broader, more inclusive

definition of “rapid” or “abrupt” used by the scientific community. This disconnect is indicative of the

way economists tend to use the notion of a climate induced catastrophe more loosely, rarely applying

the same degree of precision as found in the scientific literature. How the treatment within economic

studies lines up with the scientific community’s evaluation of which tipping points are likely to occur,

when, and on what time scale impacts will unfold is rarely evaluated. The disconnect may also in part

stem from the practice of discounting in economic models, which puts a practical limit on what is

typically viewed as catastrophic in economic terms. Economists typically measure economic damages

associated with an increase in global mean temperature in terms of the change in societal welfare or

foregone consumption in future years, discounted to the present. At positive discount rates, impacts

thousands of years in the future are quantitatively negligible when expressed in present value terms.

Other differences in the treatment of climate change induced events may stem from the role of

discounting in economic models, which places a practical limit on which events would be viewed as

catastrophic in economic terms. With a positive discount rate, the present value of social welfare losses

due to climate change will be negligibly affected by events occurring far in the future (e.g., in thousands

of years).

In this section we first examine the theoretical evidence to support the assertion that climate

catastrophes may play an important role in understanding socially efficient abatement policy. Then we

review how the economics field has chosen to model the types of events the scientific literature refers

to as “catastrophes.”

10

Economic Theory of Catastrophic Events

The importance of including low probability but potentially high impact catastrophic events in an

economic modeling framework was initially informed by the theoretical work of Cropper (1976). She

considers the generic case of a stock pollutant whose buildup reduces social welfare in a continuous

fashion up to the point where the stock crosses a threshold, at which time a discontinuity occurs and

social welfare immediately falls to the level associated with subsistence consumption. While the level of

the threshold is uncertain the decision maker has an informed prior. Within this setup she finds that the

potential for a catastrophic event can cause the presence of multiple market equilibria, suggesting the

potential existence of such events has strong policy implications.

Tsur and Zemel (1996) were among the first to translate this theoretical framework to the case of

climate change, with the stock pollutant representing atmospheric carbon, which through its impact on

Earth systems could potentially lead to a catastrophic event. As in Cropper (1976), if the stock pollutant

crosses an uncertain threshold the stream of economic damages associated with the pollutant are

instantaneously and permanently increased by a fixed amount. Tsur and Zemel (1996) extend this basic

framework to allow for adaptation where resources may be diverted from consumption towards

mitigating the impacts of the catastrophic event. Even with the potential for adaptation they find that, in

theory, the potential for catastrophic events induced by climate change could have significant policy

implications for the optimal level of abatement.

Climate change poses a unique problem for economic modeling in that mitigation requires large sunk

costs in the near term with highly uncertain benefits occurring in the far future. Given this situation

reducing the uncertainty associated with the payoff for mitigation may have tremendous value to the

policy makers. Therefore, Hendricks (1992) and Pindyck (2000), among others, have used a real options

framework to examine the characteristics of optimal climate policy given the potential for a policy

maker to learn about the expected damages over time. Assuming that the net benefits of pollution

abatement are uncertain but well represented by continuous stochastic differential equations, they

develop the basic result that the irreversible nature of abatement investments implies delaying

mitigation may be optimal. This is directly derived from the assumption that uncertainty regarding the

impact of climate change will be, at least, partially resolved over time. Baranzini et al. (2003) extend this

concept to account for the possibility of a climate related catastrophe and find that the potential for a

large scale event could theoretically offset the irreversible capital effect, negating the benefits of

delaying action. This result is derived from the assumption that the arrival of a climate catastrophe is

11

well modeled by a negative Poisson jump process with an exogenous arrival rate. This assumption

describes a catastrophe that occurs instantaneously and whose likelihood of occurring is independent of

any abatement policy adopted.

Clearly a notable concern with the work of Baranzini et al. (2003) is the assumption of unavoidable

catastrophes. As noted by Pindyck (2007), “the possibility of a catastrophe will likely increase the

expected benefit from any amount of abatement.” To understand the theoretical implications of failing

to couple mitigation efforts and the potential for a climate induced catastrophes, Fisher and Narain

(2003) extend this real options framework. They assume that “the world comes to an end after [a]

catastrophe has occurred,” or in other words, utility is instantaneously and permanently reduced to zero

in a fashion similar to Cropper (1976). When this risk is assumed to be unavoidable (i.e., independent of

abatement policies) they find the irreversible nature of investments in abatement will lead to less

abatement, a result also found by Kolstad (1996). This is because when the risk of the catastrophe is

purely exogenous it effectively acts as an increase to the discount rate. However, when the probability

of a climate catastrophe occurring is allowed to be a function of emissions its presence results in an

increase in the theoretically optimal level of abatement.

Economists have also considered the role for publically subsidized research in abatement technology in

the face of such potential events. Castelnuovo et al. (2003) use a regional model that includes

endogenous technical change to examine the role of catastrophes in optimal investment patterns. They

take a similar approach to Fisher and Narain (2003) and Bosello and Moretto (1999) such that if a

climate induced catastrophe occurs it would instantaneously bring about “the end of the world” as

represented by a permanent reduction of utility to zero. Their results suggest that the potential for such

an event increases the value of abatement capital and therefore the optimal level of research and

development investment. Based on the public good nature of the product they suggest that there may

exist a role for policy to incentivize such spending.

Catastrophic Events in Integrated Assessment Models

While the theoretical work on potential climate catastrophes and optimal policy provides strong

motivation for incorporating the possibility of such events into economic modeling, how best to reflect

them within empirical modeling frameworks remains an open question. Quantitative analysis of climate

change policy is often carried out with the aid of integrated assessment models (IAMs) that allow for a

potential bi-directional coupling of natural Earth and economic systems. Due to the complex nature of

12

these systems they are often represented in a simplified, highly aggregated form in order to keep the

model tractable from the perspective of both utilization and parameterization. For example, William

Nordhaus’ Dynamic Integrated Model of Climate and the Economy (DICE) model represents the global

implications of climate change as a proportional loss of economic output that grows by the square of the

average annual global temperature anomaly (Nordhaus and Boyer 2000, Nordhaus 2008). More complex

integrated models such as the Climate Framework for Uncertainty, Negotiation, and Distribution (FUND)

(e.g., Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009) and the Global Change Assessment Model

(GCAM) (Calvin et al., 2009) provide more detailed representations of natural and economic systems

along with finer geographic and sectoral resolution, but the additional complexity brings significant and

potentially prohibitive computational burden to some types of uncertainty analysis. In this section we

review how IAMs have been used to date to model the types of events the scientific literature refers to

as “catastrophes” (see Table 1 for a summary of studies).

Nordhaus (1994b) made one of the first attempts to incorporate the potential of catastrophic events

due to climate change into quantitative economic modeling of optimal carbon policy. This work used the

results of an expert elicitation to help parameterize the damage function of the DICE model in a way

that accounted for the probability of a climate catastrophe. The survey asked a panel of 19 experts

about the probability distribution of climate change induced damages for a series of three scenarios (3 oC mean global temperature anomaly in 2090, 6 oC in 2175, and 6 oC in 2090) (Nordhaus 1994a). One

question specifically asked about the probability of losing 25% of global world product in each of the

three scenarios receiving a median response of 0.5%, 3.0%, and 5.0% for the three scenarios,

respectively. In later versions of the DICE model this probability of catastrophic consequences was

explicitly brought into the calibration of the damage function to proxy for the expected value of climate

change impacts (Nordhaus and Boyer 2000).7 Yohe (1996) expanded on this line of research by explicitly

considering the possibility of low probability high impact events within an IAM when analyzing optimal

carbon policy. In this work an augmented version of the DICE model is run with a discrete (two state)

probability distribution that allows for the small probability of a world in which carbon emissions result

in large damages (e.g., a loss of 12.5% of GDP for an average global annual temperature anomaly of 2.5 oC compared to 1.6% at 3 oC).

7 While Nordhaus and Boyer (2000) state they are calibrating their model to the survey results of Nordhaus (1994b) the values reported in this later work to be from the survey do not match those reported in the original discussion of the survey results.

13

The choice to model climate catastrophes in a generic manner, abstracted from the specifics of the

natural science and economics of such events, has continued well beyond these initial studies. A typical

example of this trend is the work by Gjerde et al. (1999). While the sophistication of the modeling effort

has improved from earlier analyses in some aspects (e.g., implementation of regional impacts), the link

between economic welfare and the potential high impact natural event is left vague and relatively

undeveloped. To analyze the optimal GHG emissions path in the presence of uncertain but potentially

catastrophic events, they use a regionalized IAM in which a social planner maximizes an additively

separable intertemporal welfare function that is negatively affected by climate change. In an approach

that doesn’t differ substantially from that of Cropper (1976), Gjerde et al. (1999) incorporate the

possibility of climate induced catastrophe through a piecewise utility function where household well

being is instantaneously and forever reduced by a fixed amount in the event of a catastrophe, where the

probability of such an event occurring is calibrated to the expert elicitation of Nordhaus (1994a).8

A few papers improve on this generic approach by either adjusting the model to account for differences

in impacts across specific catastrophic events or to allow for the welfare effects to phase in over time.

However, while more sophisticated, these papers are few in number and still are not necessarily

predicated on the existing scientific literature. Nicholls et al (2008), for example, advance the estimation

of welfare impacts due to West Antarctic ice sheet (WAIS) disintegration using the FUND model. They

extend the typical paradigm by allowing for a basic version of endogenous adaptation through

protection measures as a function of the rate of sea level rise. However, the abstract WAIS melting

scenarios considered in this study, within as little as 100 years, appear inconsistent with the timescales

considered relevant for WAIS by the natural science community where a WAIS collapse has not been

simulated to occur in less than 1,000 years (Lenton and Ciscar 2012). See appendix for additional

discussion.

Lemoine and Traeger (2012) consider how a variety of potential catastrophic events might affect optimal

climate policy as well as the marginal social cost of carbon (SCC).9 Unlike prior studies, the authors

model the potential for two types of climate tipping points. The first increases feedbacks that amplify

the effect of emissions on temperature, and is said to be representative of rapid retreat of land ice

8 Interesting to note is that given their setup, the inclusion of a large instantaneous impact from catastrophic events (permanent reduction of GDP by 25%) lowers the optimal emissions path such that the probability of a catastrophe by 2090 is only reduced from 4.8% in the business as usual case to 4.0% in the policy case. 9 The social cost of carbon represents the discounted present value of the welfare losses associated with the release of an additional metric ton of CO2 into the atmosphere in a given year.

14

sheets or climate induced releases of methane deposits. The second increases the atmospheric lifetime

of CO2, which is said to be representative of weakening of carbon sinks. They use a modified version of

the DICE model which allows them to consider both parametric uncertainty in the temperature

threshold that will trigger a given catastrophe, and stochastic uncertainty in the temperature dynamics.

The impact of each possible catastrophe is modeled differently such that a doubling of the equilibrium

climate sensitivity is used to represent large climate feedbacks from a rapid retreat of land ice sheets or

releases of methane deposits a decrease in the modeled decay rate of atmospheric CO2 represents a

weakening of carbon sinks. While this study represents one of the most sophisticated modeling

exercises used to examine the policy implications of potential catastrophic events to date, it still uses

the traditional assumption that passing a given climate threshold results in an instantaneous and

permanent shock to the system. The study improves on the previous literature by not forcing all

modeled catastrophes as a direct shock to welfare, but, like Nicholls et al. (2008), follows the traditional

approach in which the assumptions regarding the magnitude of the effects are ad-hoc and not

developed through a rigorous scientific assessment. In addition the modelers only consider one

potential catastrophe at a time and assume that the trigger is reached around 2040 (it is modeled with

uncertainty so this is the central point of the distribution) under the no policy reference case,

independent of which of the four potential catastrophes is being considered.

Cai et al. (2012) take a similar approach to Lemoine and Traeger (2012) and use a stochastic version of

the DICE model to consider the impact of potential tipping points on optimal carbon policy. As with

much of the previous work they assume that in the event of crossing a tipping point there will be an

instantaneous and permanent economic shock, specifically they model the outcome as a permanent

upward shift in the damage function. The occurrence of a catastrophic event is assumed to be uncertain

and is modeled as a jump process where the hazard rate is a function of the current temperature

anomaly and is calibrated using the expert elicitation results in Zickfeld et al. (2007) and Kriegler et al.

(2009). However, the economic impact of a catastrophic event (i.e., the shift in the damage function) is

chosen ad-hoc to range from an additional 2.5% to 10% of GDP.10 Like previous work in the area they

find that the optimal climate policy may be substantially influenced to the potential for catastrophic

events, and add to the state of literature by showing that the result is sensitive to the way in which risk

aversion is captured within the modeling framework.

10 Therefore as modeled it is possible to have a world with a global and annual mean temperature anomaly of 2.5 oC suffer climate damages of 1.8% of GDP and then the next instant face damages of 4.3%-11.8% of GDP if an event occurs.

15

One of the few examples where a potential climate catastrophe has not been modeled as an

instantaneous shock is the work of Hope (2011), which uses the PAGE09 integrated assessment model.11

PAGE09 models a generic potential climate catastrophe, where the probability of such an event

occurring is zero until a given threshold is reached after which point the probability begins to rise. If a

climate catastrophe is triggered, there is a permanent reduction in welfare. However, unlike prior

versions of the model, it is not instantaneous and instead there is a transition period over which the

welfare impact is phased in. Since the model only incorporates a single generic potential climate

catastrophe the transition period is considered uncertain with a range of 20 to 200 years. The

probability that an event occurs and the range of welfare impacts considered are both chosen in a fairly

ad-hoc manner. The lower end of the range for potential welfare losses in the European Union are based

on potential sea level rise damages studied in Anthoff et al. (2006), however no justification is provided

for the upper end of the range. Furthermore, potential damages in other regions are primarily based on

the length of their coastline relative to that of the European Union.

While most studies have chosen to model a generic event, when a specific event is considered the most

popular one has been the potential for a shutdown of the Atlantic thermohaline circulation (THC). Link

and Tol (2011) use the FUND IAM not to directly examine the policy implications of a potential

shutdown of the THC, but to estimate the welfare impacts if such an event does occur. They use

experiments conducted with an atmospheric and ocean general circulation model (GCM) to determine

the impact that a shutdown of the THC would have on regional temperature anomalies and feed this

information into a nationalized version of the FUND model.12 This setup produces estimates of the

additional welfare loss that would be experienced in the event of a shutdown of the THC, assuming the

impact of the shutdown begins in 2070 and increases linearly until its full effect is reached in 2100 (see

the Appendix for more detail).

Ceronsky et al. (2011) consider a similar experiment, but consider the policy implications more directly

by examining the effect on the SCC. Specifically, they use an updated version of the FUND model to look

11 The PAGE (Policy Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decision-makers in assessing the marginal impact of carbon emissions. In the previous version of the PAGE model, PAGE2002, a generic potential climate catastrophe was modeled as an instantaneous and permanent reduction in welfare, where the probability of such an event occurring was zero until a given threshold was reached after which point the probability would begin to rise (Hope 2006). 12 The study of Link and Tol (2011) provides three extensions to their earlier study (Link and Tol, 2004) in that they move from a regional to national scale for modeling welfare impacts of climate change, use a more sophisticated atmospheric and ocean GCM to determine the temperature impact of a THC shutdown, and no longer assume that such impacts will be felt instantaneously.

16

at the effect of a potential weakening of the THC and large scale methane releases from the deep ocean

on the SCC, assuming we know one of these events will occur with certainty. Similar to an earlier study

by Link and Tol (2004), they represent the impact of a THC shutdown by adjusting regional temperature

anomalies using the results of Ranhmstorf and Ganopolski (1999). To test the impact of a potential large

scale methane release from the deep ocean they assume that starting instantaneously in 2050 methane

emissions will increase by a fixed amount. Both the time of the instantaneous shift in emissions and the

level of the shift are based on the judgment of the researchers, and sensitivity analysis is only conducted

around the level of the shift.

Keller et al. (2000) estimate the costs associated with preventing a shutdown of the THC. They

determine a threshold level of atmospheric carbon, based on the work of Schmittner and Stocker

(1999), beyond which the THC would collapse. They then run the DICE model subject to the constraint

that this event cannot occur. In a post processing step Keller et al. (2000) compare the additional cost

associated with meeting this constraint to an ad-hoc estimate for the welfare loss associated with a

shutdown of the THC in order to assess the social optimality of preventing this potential climate

catastrophe. While the welfare impacts of the catastrophe are not endogenous to the model the

additional benefit-cost analysis is conducted under the implicit assumption that after passing the

threshold, social welfare will be instantaneously and permanently reduced.

Mastrandrea and Schneider (2001) address one of the primary caveats of Keller et al. (2000) by allowing

the welfare impacts of changes in the overturning of the northern Atlantic Ocean to be endogenous

therefore allowing them to estimate the optimal carbon policy given the presence of this potential

catastrophe. Like Keller et al. (2000), Mastrandrea and Schneider (2001) start with the DICE model but

expand on the previous analysis by allowing the THC shutdown threshold to be a function of both the

carbon stock and the rate at which the stock is increasing. This addition is said to account for the

possibility that rapid increases in the carbon stock could overwhelm the ocean’s ability to dilute surface

water through mixing with the lower ocean. Furthermore, Mastrandrea and Schneider (2001) allow for

the possibility of a partial shutdown of the THC that feeds back into the damage estimates within the

DICE model. In their study the uncertainty is not over the location of the threshold associated with the

natural event but with the social welfare losses that would result from such an event, such that a full

shutdown of the THC could result in an additional loss of between 1% and 25% of global GDP above the

baseline climate damages. While this work provides a representation of the Earth system change that is

more firmly rooted in the scientific literature, the welfare impacts of the potential event are set forth in

17

an ad-hoc manner. The range of welfare impacts is not based on the results of a damage assessment

and no judgement is made about the likelihood of any of the cases studied.

In commenting on the social and policy implications of potential climate catastrophes, Hulme (2003)

noted that there were no estimates for the social welfare loss associated with such events that are

grounded in “substantive environmental, economic, or social research.” While Hulme (2003) was

particularly focused on the example of a THC shutdown, our review suggests that most economic studies

looking at the policy implications of potential climate catastrophes have failed to define the welfare

losses from such events in a rigorous fashion. However, a few new studies have sought to move forward

by developing welfare loses in response to occurrence of a climate induced catastrophe using bottom-

up analyses (e.g., Link and Tol 2011, Ceronsky et al. 2011). The economic literature has also typically

fallen short when it comes to incorporating the latest natural science on potential climate catastrophes.

Lenton and Ciscar (2012) note that there is currently a “huge gulf between natural scientists’

understanding of climate tipping points and economists’ representations of climate catastrophes in

integrated assessment models.” Such a criticism appears warranted given that the most commonly

applied description of a climate catastrophe is an event which occurs instantaneously as a result of

crossing a given threshold, after which part of the system (typically welfare) is permanently altered by a

fixed quantity. Therefore while the economic modeling exercises to date provides evidence that

potential climate catastrophes might significantly influence the optimal path of abatement, they do not

provide results sufficiently grounded in natural and economic science to meaningfully inform the policy

debate.

4. Moving Forward

Within the economics literature climate induced catastrophes have been modeled by most researchers

as equivalent to a large, permanent, and instantaneous impact on social welfare once a critical Earth

system threshold is crossed. While the common failure to rigorously calibrate these exercises is of

particular concern, even more startling is the fact that such a specification bears little resemblance to

the potential climate catastrophes discussed within the scientific literature. There has been some

progress recently in improving the estimation of welfare losses from potential climate catastrophes (e.g.

Ceronsky et al. 2011; Link and Tol 2011), but these efforts have focused on the potential weakening of

the THC, an event that is considered less likely to occur than many other large scale earth system

changes (Kriegler et al. 2009). To help lay the foundation for improving the economic modeling of

18

potential climate catastrophes for use in policy analysis, this section briefly describes what is known

about often discussed potential Earth system changes that fall within the definition of climate

catastrophe as used by the scientific literature. Based on this review we offer thoughts on potential near

term modeling improvements that would allow for enhanced quantitative analyses of climate

catastrophes in the economics literature.

Our review of potential large scale Earth system disruptions and their associated physical impacts is

intended to provide a summary of what information is available for modelers. We consider what the

scientific literature has written about issues that are particularly relevant to economic analysis in terms

of being able to both better model these impacts and prioritize which ones may initially command more

attention. Such relevant characteristics include which events are considered more or less likely to occur,

whether there exists more or less scientific consensus on how and when physical impacts will unfold,

and which physical end points have probabilistic projections defined. This summary is written from the

perspective an economist and is not intended to be an assessment of the scientific merits of particular

studies. It is intended to help modelers identify potential climate catastrophes that can be better

incorporated into IAMs now, and where additional research and modeling work is needed for others. It

is worth emphasizing at the onset that there is still a great deal of uncertainty even for events that are

viewed as having a higher probability of occurrence. Though there may be a paucity of data in many

cases, this section highlights that there appears to be enough information available to significantly

improve the way in which climate catastrophes are represented in economic analyses.

Overview of Potential Climate “Catastrophes”

The starting point for our review is a set of 15 often discussed large-scale Earth system changes that may

be induced as a result of climate change (see Table 2). Many of these have been characterized in the

scientific literature as exhibiting “tipping point” behavior in that once a critical threshold is crossed for

some control parameter, the system will be qualitatively altered and often cannot go back to its original

equilibrium.13 While the details of our review are provided in the Appendix, Table 2 offers a brief

description of each potential “catastrophe” and two key characteristics: the level of global warming

13 The set of potential events in Table 2 is not meant to be exhaustive, but representative. See Lenton and Ciscar (2012) for a discussion of some additional large scale events that might occur as a result of climate change, including the North Atlantic sub-polar gyre and aridification of southwest North America. Also note that in this paper we do not attempt to offer our own definition of “climate catastrophe” or “tipping points”. Rather our goal is to review the evidence on large scale Earth system changes as a first step to improving how well they are captured in IAMs.

19

needed to trigger the event and the timescale over which the transition to a new state is expected to

occur. Even this small amount of information highlights the considerable variation across potential

climate catastrophes. First, some changes are primarily a direct result of increasing temperatures (e.g.,

ice sheet melt), while others hinge on changes in precipitation patterns, ocean temperature gradients,

and/or a complex combination of mechanisms (e.g., changes in ENSO, West African monsoon). Second,

based on Lenton et al.’s (2008) assessment of the amount of warming needed to pass potential critical

thresholds (measured relative to 1980-1999 temperatures), it is possible that some thresholds may have

already been crossed (e.g., loss of Arctic summer sea ice). However, it also appears that in a majority of

the cases a significant level of additional warming would be required to trigger an irreversible shift in the

equilibrium of these systems (though within the expected level warming over the next couple centuries

given business as usual emissions). Third, there appears to be considerable variation in the estimated

timescales over which physical impacts are expected to unfold, where the full impact may not be

realized for decades, centuries, or even millennium. In quite a few cases, even if a critical threshold is

transgressed, the effects – and particularly their full impact – are a long way off. This information also

reinforces the observation that these potential events are poorly represented in most IAMs when they

are modeled as a low probability of an instantaneous change in global welfare.

Finally, it is important to note that the events listed in Table 2 are not necessarily independent of each

other, such that the occurrence of one can increase the risk of others being realized. For example,

changes in the frequency or magnitude of the El Nino Southern Oscillation (ENSO) will likely affect

precipitation patterns in South America and thus influence the probability of massive dieback of the

Amazon rainforest. Similarly, a collapse of the West Antarctic Ice Sheet (WAIS) is expected to encourage

thawing of permafrost and in turn additional melting of the Greenland Ice Sheet (GIS). The full set of

feedbacks between these fifteen Earth systems is too complex to summarize in the Table, but Lenton

and Ciscar (2012) provide an overview of many of the critical linkages identified to date. It is also

possible for a combination of changes to cause a planetary shift to occur even if no single boundary or

tipping point is transgressed (Barnosky et al. 2012). It is for this reason that Lenton and Ciscar (2012) call

on IAM modelers to consider all tipping points together instead of in isolation. While it may not be

possible to adequately include all tipping points initially, nor desirable to postpone analysis until this is

possible, it at least suggests that after analysts study one potential catastrophe they do not remove it

from the model before proceeding to the next potential event.

20

Potential for Near Term Modeling Improvements

Given the wide variation in the types of potential climate catastrophes, an important question is which

of these possible events are the most decisive and/or feasible to better represent in IAMs? The

categorizations or rankings provided in scientific review articles may appear to provide an easy starting

point to prioritizing research efforts. Table 3 considers a few such notable categorization efforts. Lenton

et al. (2008) suggested their own new categorization such that “policy relevant tipping points,” are those

Earth system changes that may be triggered within this century – on the “political time horizon” –

compared to those that would undergo a qualitative change within this millennium, which they denote

as the “ethical time horizon.” It is important to note that this definition of a political time horizon is

based on the crossing of a threshold and not necessarily the time frame under which the impacts would

become realized. Lenton’s (2011) assessment of relative likelihoods and impacts are based on a five-

point scale: low, low-medium, medium, medium-high and high. His likelihood rankings are based on his

reviews of the literature and expert elicitation (Kriegler et al 2009). Impacts are based on limited

research (Lenton et al. 2009) and subjective judgment, and are rated relative to the one system (THC)

with multiple impacts studies. Impacts are considered on the full ‘ethical time horizon’ of 1,000 years,

assuming minimal discounting of impacts on future generations. Allison et al. (2009) provide a

categorization of potential climate catastrophes that are “of greatest concern” meaning those that are

“the nearest (least avoidable) and those that have the largest negative impacts.” This assessment is also

based primarily on Kriegler et al. (2009) and other existing reviews (e.g., Lenton et al. 2008, Lenton

2009).

Although these categorization efforts are potentially helpful as a first cut, care should be taken in using

them to prioritize IAM efforts because they may not match up with what is most relevant to modeling of

economic consequences that meaningfully inform policy analysis today. For example potential events

that require a significant amount of warming, have multi-century transition times, and a low likelihood

of occurring are less likely to produce rapid, significant near-term economic damages than ones that

have low temperature thresholds, short transition times, and are less uncertain. Also, a focus on tipping

points should not come at the expense of improved representation of large-scale Earth system changes

that, although not expected to exhibit tipping point behavior, are likely to have gradual, sometimes large

physical impacts with relatively high probability.

In Tables 4 and 5 we begin to take a closer look at each event to help prioritize which potential climate

catastrophes are most appropriate and feasible to analyze initially in economic models. We consider

21

details such as the types of physical endpoints that would be impacted, the degree of scientific

consensus around even the basic characteristics of how these impacts will likely unfold, the shape of the

transition dynamics, and data availability. This summary is based on a careful review of the scientific

literature, the details of which are in the Appendix.

First, Table 4 assesses which of these potential Earth system changes are expected to produce significant

physical impacts in the near term (e.g., in this century) and summarizes the availability of scientific

projections of key physical endpoints relevant to each event. This information is important for

understanding which events are more or less likely to produce economic damages within the next

century – thus making them more relevant to incorporate into present day policy analysis. This is not to

say that economic research on the more uncertain or distant potential climate catastrophes is

unimportant, but rather to highlight the areas where improved modeling of the physical and economic

impacts is more feasible in the near term and at the same time are likely to have the greatest

implications for current policy analysis.

We find that regardless of the degree of certainty about the existence of a tipping point and location of

critical threshold for each of these events, important large-scale changes in many of these Earth systems

are expected within this century even under moderate warming scenarios. Changes in some systems are

already occurring and projections are becoming available for a number of physical endpoints that may

allow for improved climate and natural system modeling of these events even within current IAM

frameworks (e.g., permafrost thaw). The degree of consensus about how the physical impacts are

expected to unfold varies greatly across the potential climate catastrophes. Our review shows that of

the 15 identified potential climate catastrophes, there is relatively more scientific consensus regarding

the impacts of about half of them. By consensus we mean a general understanding of how Earth

systems will respond (e.g., which physical endpoints will be affected and the direction of impact on

these endpoints) rather than scientific agreement on the detailed modeling and projections of physical

impacts. For example, in several cases the scientific uncertainty is primarily with regard to the

magnitude and rate of change (e.g., sea level rise from ice sheet melt). For other events there is still

considerable debate not only on the magnitude and timing of the Earth system change but even on the

direction of change. For example, although many of the mechanisms and physical feedbacks that control

the characteristics of the El Nino Southern Oscillation (ENSO) are expected to be affected by rising GHG

emissions, recent assessments find models are highly inconsistent with respect to their projections of

change in ENSO amplitude, frequency, and variability. Some models show an increase in the amplitude

22

of ENSO variability in the future, others show a decrease, and some show no statistically significant

changes. Similarly, the debate over the vulnerability of the Atlantic Thermohaline Circulation (THC) is

still far from settled. Some studies consider a weakening of the THC to be much more likely to occur

than a complete shutdown, with the rate of warming being a critical factor, and even among models

predicting an anthropogenic weakening of the THC, the impacts are not expected to be imminent. (See

the Appendix for more discussion of both the ENSO and THC literature.)

The first step to improved representation of these potential events in IAMs is improved representation

of the key physical endpoints through which economic consequences are most likely to be experienced.

Table 5 summarizes our assessment of these key endpoints, as grouped into four general categories:

temperature, sea level, precipitation, and extreme events. We have also added an “other” category

which, for many events, captures whether the economic consequences will be a result of ecosystem

impacts (e.g., vegetation/forest cover impact, species loss), but can also include other changes (e.g.,

opening of trade routes from sea-ice loss, direct health impacts from ozone hole). Shaded cells indicate

physical endpoints that have received the most attention by scientists – either because they are

expected to be the largest/most significant sources of economic damage associated with the

catastrophic event or because more is known to date about how the physical impacts will evolve.

Our assessment is highly simplified as many details and complex interactions between events are not

captured in this table,14 yet it provides a useful starting point for improved reduced form representation

of some potential climate catastrophes in IAMs. For example, it highlights for modelers the appropriate

physical endpoint(s) through which the potential catastrophic events should be incorporated into the

IAM framework. Though depending on the IAMs completeness in terms of linking physical to economic

endpoints, the modeler may be required to develop mappings of how these physical changes relate to

economic damages. In a similar vein, Table 5 also highlights the need for more explicit representation of

certain physical endpoints in IAMs. The current generation of reduced form IAMs each take a somewhat

different approach to damage functions, but in all cases the majority of damages are based on changes

in global and annual mean temperature and sea level rise. However, for many of the potential climate

induced catastrophes additional physical endpoints, poorly correlated with changes in global

temperature, such as precipitation and those associated with extreme weather events are critical for

capturing their impacts. One needs to be careful in recognizing that incorporating these additional

14 Lenton and Ciscar (2012) summarize some physical impacts not included in Table 5, such as impacts on atmospheric and ocean circulation, and interactions between events.

23

physical systems is only part of the task, as changes must then be translated into welfare impacts, which

remains a challenging issue.

Ideally, modelers would have access to studies that provide information on the path of changes in the

physical endpoints listed in Table 5 and any other relevant Earth system impacts over time, the

distribution around that path, and the correlation with climate variables/other tipping points/large scale

feedbacks for each of the potential climate catastrophes. With such data modelers would be able to

credibly represent the impacts that these potential climate catastrophes will have on natural system

endpoints and begin to map them more explicitly to economic damages.15 Our review suggests that the

scientific literature is far from being able to provide all of this information but, as was shown in Table 4,

in some cases it appears a richer set of data already exists that could readily be incorporated into IAM

modeling efforts.

For example, in the case of permafrost thaw numerous studies have projected change in active layer

depth and extent of permafrost area for the 21st century (and beyond), and forecasts of the magnitude

of the accompanying carbon feedback are also becoming available (Schaefer et al. 2011). The thawing of

permafrost was not ranked highly in the scientific reviews included in Table 3 due to its lack of a specific

tipping point. However, this is an event that is expected to occur (and may already be occurring) in this

century, for which there is relatively more scientific consensus regarding its impacts, and the primary

endpoints are already captured within most current IAMs. Therefore permafrost climate feedbacks

seem to be an ideal candidate for inclusion into the current modeling frameworks. While multiple

economic studies have mentioned the thawing of permafrost as a possible source of catastrophic or

abrupt climate change, the most “advanced” study to model this type of event (Lemoine and Traeger,

2012) considers it to be a fixed, instantaneous and permanent doubling of the equilibrium climate

sensitivity. Our review indicates that a more explicit representation of the additional carbon flux from

thawing permafrost and associated damages from the resulting additional warming is possible, based on

available projections from Schaefer et al. (2011) or similar studies. The currently available reduced form

IAMs have the capacity to incorporate the magnitude and rate of this carbon feedback effect, and are

already designed to account for the welfare impacts of additional releases of carbon emissions. While

more research and modeling is needed to incorporate other damage categories (e.g., valuation of

15 In order to assess the welfare implications of non-marginal policies with the use of IAMs inclusive of potential climate change induced catastrophes, the endogeneity of these physical endpoint changes to anthropogenic emissions would have to be established within the models.

24

ecosystem impacts from permafrost thaw), an initial study correctly capturing the timing and

quantitative welfare impacts is currently feasible.

In the case of a potential Amazon dieback there also exists a richer set of data on physical impacts that

could be incorporated into IAM efforts. For example, relevant physical endpoints for which 21st century

projections are available include: change in tree cover, vegetation and soil carbon, precipitation,

amplified regional warming (e.g. Cox et al. 2000, 2004). Rammig et al. (2010) go so far as to estimate

probability density functions for change in vegetation carbon storage (kg C m-2) by Amazonian region

for 2070 –2100 vs. 1970–2000 using the variation in GCM rainfall projections and sensitivity to CO2

fertilization. Although it would require more work than permafrost thaw, some basic incorporation of

this latest scientific research into IAM modeling of Amazon dieback seems feasible.

In some cases, the scientific literature may at least provide plausible bounds on the size or speed of

impacts. For example, the latest research suggest a reasonable range for total sea level rise from all

sources to be about 0.5-2 meters by 2100, with a lower likelihood assigned to the upper end of this

range (Nicholls et al. 2011), and zero probability of sea level rise exceeding 2m by 2100 (with at most

about 50-60cm coming from either ice sheet) (Pfeffer et al. 2008). Since in most IAMs some structure

already exists to measure welfare impacts to changes in sea level rise, better modeling of the dynamics

of sea level alone, as is already done in some newly released IAMs (e.g., Nordhaus 2010), will help to

improve representation of catastrophic ice sheet loss. Simplified representation of some impacts of

melting summer sea ice may also be possible. The complete loss of summer sea ice in the Arctic is one of

the most widely expected Earth system changes examined in this paper, yet, to our knowledge, none of

the IAMs currently model the damages associated with feedback effects of this loss. It appears the

results of studies examining the regional temperature and weather pattern impacts of sea ice loss (see

Appendix) could be brought to bear on the economic damages resulting directly from temperature (and

perhaps precipitation) changes. Models of the economic impacts associated with improved accessibility

of Arctic harbors (e.g., for resource extraction, shipping) could also begin to be developed based on

existing projections of sea ice extent.

As mentioned previously, for several potential climate catastrophes the primary Earth system end points

that are expected to be impacted are not typically modeled within the current generation of IAMs.

Therefore, even if these potential events are judged to be of great concern in scientific reviews such as

those included in Table 3, improved modeling of the physical and economic impacts may be less feasible

25

in the near term as it will require improved representations of other Earth system changes, which could

be a relatively difficult process in some cases. For example, most IAMs used to estimate the welfare

impacts of climate change are not currently designed to directly assess the effects of changes in

precipitation or intra-annual weather variability. Therefore in the case of potential changes to the ENSO,

there may be a need to better incorporate additional Earth system endpoints and their associated

welfare connections into the model before the potential climate catastrophe can be adequately

represented. Similarly, modeling of a weakening/collapse of the West African Monsoon

(WAM)/greening of the Sahel would also require additions to the currently available models.

Finally, those Earth system changes that are not likely to manifest until very far out in time may be of

lower priority than others because they are outside the scope of what economic models typically focus

on (pre-2300?). For example, a massive release of methane from sea floors would lead to significant

amplified global warming effects. However, the timescale of the forcing needed for this to occur is

assessed to be over 1000 years off because it will take that long for the sediment to warm to the point

of reaching the hydrate deposits.

We suggest caution even in the cases where the models currently represent the affected Earth system

endpoints and their welfare implications. Most of the existing models predicate welfare impacts on the

level of the change in Earth system endpoints, and do not explicitly account for the rate at which those

changes occur. In general, the more gradual the shift, the more likely it can be captured within the

existing framework of even a very reduced form IAM. However, the more quickly the event is expected

to speed up another change (i.e., rapid increase in the rate of temperature growth or sea level rise) the

more important it will be for the model to incorporate how vulnerability and adaptation possibilities can

be affected by the rate of change. This is a widely acknowledged limitation of the current suite of

models available as they exhibit very limited, if any, opportunity for endogenous adaptation. In the case

of potential climate catastrophes one must be concerned not just with the presence of endogenous

adaption, but the whether realistic “time-to-build” constraints are implemented.16

16 Another note of caution is that the current suite of models may be limited in modeling the implications of irreversible tipping points. It has been shown generically in the economics literature that the irreversibility associated with crossing the threshold may have implications for the value of GHG mitigation policies (e.g., Fisher and Narain (2003), Pindyck (2000)), but the techniques for computing the implications of irreversibility may currently be computationally infeasible when working with more detailed IAMs that have been modified to more accurately represent the effects of large scale Earth system changes. Simpler models commonly used to explore the importance of irreversibility may be need to be calibrated to more detailed IAMs that more accurately represent potentially catastrophic climate changes in order to develop an understanding of the magnitude, timing,

26

5. Closing Thoughts

A common question regarding integrated assessment models used to assess the benefits of GHG

mitigation policies is how well they capture the potential for “catastrophic” events induced by climate

change. While this question has in part been motivated by earlier economic research on the potential

policy importance of a generic and abstract event that occurs with a low probability, a careful review of

the scientific and economic literature suggests there is an error in translation when modelers

incorporate the climate “catastrophe” work of natural scientists into IAMs. In the scientific literature

emphasis has been placed on the Earth systems that are associated with tipping points. This focus

appears justified given that a relatively abrupt, irreversible (at least on relevant time scales) move from

one equilibrium to a distinctly different equilibrium by definition represents a large scale change for that

system itself. Through interactions between natural systems such changes could also represent large

scale changes for the Earth system as a whole. In the context of the economics literature and the

benefit-cost analysis of GHG mitigation policies, the question becomes one of the economic impacts of

these Earth system changes. Thus far, the majority of efforts to understand the benefits of GHG

mitigation when there exists a possibility of large scale Earth system changes have grouped all possible

changes into a single “catastrophic” damage category that has a low probability of being realized.

Motivated by modeling convenience and scientific descriptions of such changes as being relatively

abrupt, the economics literature has commonly assumed that in the event this catastrophe is triggered

there will be an instantaneous and permanent reduction in global welfare. In short, the economics

community has loosely interpreted scientifically ‘abrupt climate change’ to mean ‘instantaneous change

in welfare.’

While the economic research efforts to date are informative with regard to the potential importance of

including such large scale events in the analysis of GHG mitigation benefits, the generic and abstract

form of the “catastrophe” implemented has led to a lack of specific policy implications. One reason for

this is the lack of attention to policy relevant timescales. For scientific audiences, timescales of relevance

are defined by the speed of an Earth system shift relative to the timescale of the previous stable state,

so something as long as 100 or even 1,000 years could be considered abrupt. For an economic analysis

such time scales are not likely to be considered policy relevant or well represented by an instantaneous

regime shift. The shape of the transition during the policy relevant time horizon therefore becomes and likelihood of the effects. This would allow the community to develop estimates of the impacts of irreversibility accounting for the uncertainty of such changes and the sunk costs of mitigation policies to determine their joint effect.

27

particularly relevant to understanding the policy implications of potential large scale Earth system

changes due to climate change. Second, there is often no explicit representation of the geographic

extent over which these impacts may be experienced. Such detail is important to understand how Earth

system changes will interact with the differing vulnerability and adaptation possibilities across regions.

The third, and perhaps clearest, issue at hand is that the economics literature has often modeled the

economic impact due to a large scale Earth system change in a relatively ad hoc manner. There have

been relatively few endeavors to determine what the expected economic impacts would be given the

relevant changes in particular Earth system endpoints. Some economics research has chosen to carefully

examine the details of particular Earth system changes (e.g., Link and Tol’s (2011) study of THC

shutdown) and have started to account for how these changes interact with adaptation measures (e.g.,

Nicholls et al.’s (2008) study of WAIS disintegration). However, these studies are in the minority and only

provide a picture of the damages associated with one possible event at a time.

As researchers make strides to more explicitly and accurately represent Earth system changes and

feedback effects that have until now often been grouped together in a catch all “catastrophic impact”

category, a closer look at the scientific literature can help modelers understand which ones could more

readily be modeled given the current state of IAMs and in other cases what improvements to modeling

frameworks would be required to include additional large scale Earth system changes. It is also

important to keep in mind that the underlying goal of this line of research is to better understand the

benefits of GHG mitigation policies given the possibility of large scale economic consequences over the

next few centuries due to climate change. Therefore, we recommend that effort be applied to develop

modeling improvements not only in the way IAMs characterize Earth systems and the possibility of large

scale changes that may or may not be have an associated tipping point, but also in the way they

characterize uncertainty over the availability of adaptation or interaction between sectors that could

lead to potential large scale economic consequences. The existence of a threshold in a natural system is

neither a necessary nor sufficient condition for an event to have potentially important global or large

regional impacts within this century. It is unclear without further research that carefully models the

natural and economic systems and their interactions, whether the most policy relevant Earth system

changes are those associated with tipping points that may be crossed this century or are those

associated with more gradual but significant feedback effects that also are currently not represented in

IAMs used for policy analysis.

28

Appendix: Review of evidence on fifteen potential climate “catastrophes”

Loss of Arctic Summer Sea Ice. It is widely accepted that the Arctic region will warm considerably due to

climate change. One of the most immediate consequences of higher atmospheric temperatures and

numerous feedback effects (e.g., reduced Arctic summer snowfall) is the melting of summer sea ice in

the Arctic Ocean, which then through feedback pathways plays a central role in the region’s

temperature amplification (Screen and Simmonds 2012). The complete loss of summer sea ice in the

Arctic is one of the most likely Earth system changes listed in Table 2 (Lenton et al. 2008, Lenton 2011).

Observations using satellite data show a loss in the extent of permanent summer sea ice over past

decades. The IPCC (2007) reported decreases of 7.4 [5.0 to 9.8] % per decade since 1978, and more

recent observations suggest the decline has been even faster, at a rate of greater than 11% per decade

(Kattsov et al. 2010), with the area of summer sea ice now about one third smaller than the average

over 1979 to 2000 (AMAP 2011) and typically 40% smaller in recent years than in the 1980s (Stroeve et

al 2012). Numerous studies project this trend will continue with recent estimates predicting nearly sea

ice free Arctic summers by as early as the 2030s (e.g., Wang and Overland 2009, AMAP 2011, Zhang

2010). Many global circulation models forecast this to be a non linear transition (Lindsey and Zhang

2005), but others show a more linear loss and there is little consensus that a common critical threshold

of warming can be identified for this change (Holland 2006, Lenton et al. 2008, Kerr 2009). Lenton et

al.’s (2008) overall assessment is that the critical global mean temperature change needed to trigger the

sea ice disintegration is about 0.5–2°C global warming and suggest a rapid transition time of about 10

years to an ice free state. Wang and Overland (2009), Zhang (2010), and Kattsov et al. (2010), among

others explore the uncertainties in the magnitude and timing of sea ice loss, variation in projections (due

to both within-model contributions from natural variability and between-model differences), and why

projections are still smaller than recent observations. Figure 1 shows the Arctic September sea-ice

extent from observations and projections from 13 models included in the Coupled Model

Intercomparison Project (CMIP3) (Meehl et al. 2007), and a multi-model ensemble. Using six models

under IPCC emission scenarios A1b and A2, Wang and Overland (2009) find the median time to

transition from the current sea ice extent (4.6 M km2 in recent years) to ice free summers (less than 1.0

M km2) is 30 years (2037) with the overall mean at 32 years, and quartiles at 21 and 41 years. The

29

record low sea ice observed in September, 2012 (3.41 M km2 (or 1.32 M mi2))17 is leading some

scientists to contend that even these projections may be overly optimistic.18

Figure A1. September Sea Ice Extent, Observations and Projections*

*This figure is taken from Kattsov et al. (2010). It displays observed Arctic September sea-ice extent (thick red line) and 13 CMIP3 models, together with the multi-model ensemble mean (solid black line) and one standard deviation range of model estimates (dotted black line). Models with more than one ensemble member are indicated with an asterisk. Note that these are September means, not yearly minima.

The primary physical impacts of an ice free Arctic Ocean include amplified warming (Screen and

Simmonds 2010), large scale wind and weather pattern changes over the Northern hemisphere (NOAA),

and ecosystem changes (e.g., threatened marine mammals (Kovacs et al. 2010) – esp. polar bears,

walruses, potential increases in biological productivity (NASA 2003). Less summer sea ice will increase

the amount of solar heat absorbed into the upper ocean which will then be released back to the

atmosphere, increasing atmospheric temperatures. Some climate modeling studies find these

temperature impacts will be more limited to the Arctic itself. For example, Deser et al. (2010) find the

impact of future Arctic sea ice loss on air temperature and precipitation are greatest in November–

17 National Snow and Ice Data Center, September 19, 2012, Press Release: Arctic sea ice reaches lowest extent for the year and the satellite record. http://nsidc.org/news/press/2012_seaiceminimum.html . 18 http://in.reuters.com/article/2012/08/30/climate-arctic-idINL6E8JTH2620120830 .

30

December over Siberia and northern Canada, with late 21st century (2080-99) values ~7oC and ~0.16 mm

per day higher, respectively, than in the late 20th century (1980-99). Others show impacts on weather

and storm tracks over wider areas (Serreze and Barry 2011). For example, the higher temperatures can

elevate pressure surfaces over the North Pole into early winter and may impact large scale wind

patterns, potentially allowing cold air to move southward and produce unusually cold winters in the

eastern U.S. and eastern Asia, and cooler than usual weather in late winter from Europe to the Far East

(Honda et al. 2009, Strey et al 2009, Francis et al. 2009, Budikova 2009, Petoukhov and Semenov 2010).

One of the economic impacts associated with these changes is improved accessibility of Arctic harbors.

This could reduce the costs of exploitation of oil, natural gas, and minerals in the Arctic, and potentially

open new transport routes between Europe and East Asia. The USGS estimates that substantial off shore

reserves of oil and natural gas are yet to be discovered in the Arctic.19 With less sea ice, more of these

reserves may become accessible, although studies caution against being too optimistic about the ease of

navigation in the Canadian Northwest passage due to hazardous conditions induced by the sea ice loss

(e.g., Wilson et al. 2004, Stewart et al. 2007). The harsh environmental conditions, along with other

economic and political challenges, environmental stewardship and regulatory permitting will likely affect

timelines for exploration and production of Arctic resources, and make oil and natural gas projects in the

Arctic more expensive that similar projects in warmer areas for some time to come.20

The physical changes outlined above suggest other, perhaps nearer term, economic impacts could

include Northward expansion of commercial fishing21, energy demand impacts in parts of the Northern

hemisphere, increased coastal erosion from wind/waves/storms, non-use values for ecosystem changes,

and damages associated with amplified warming. Tol (2009) expects the positive impacts associated

19 USGS estimates that 90 billion barrels of oil, 1,669 trillion cubic feet of natural gas, and 44 billion barrels of natural gas liquids may remain to be found in the Arctic, of which approximately 84 percent is expected to occur in offshore areas See http://pubs.usgs.gov/fs/2008/3049/fs2008-3049.pdf. 20 Development of reserves is more challenging and costly in these regions due to factors such as: the need for equipment that can withstand frigid temperatures, additional site preparation costs to keep equipment and structures stable on poor soils for onshore projects, greater difficulty of existing technology to handle offshore oil spills in Arctic waters with ice flows, long supply lines and limited transportation access to manufacturing centers, higher wages to retain workers in isolated areas, etc. One study of onshore oil and gas projects in Arctic Alaska found them to cost 50 to 100 percent more than similar projects undertaken in Texas. See http://www.eia.gov/oog/info/twip/twiparch/111221/twipprint.html, http://www.eia.gov/todayinenergy/detail.cfm?id=4650. 21 However, management of new fisheries will not be trivial. The United States closed nearly the entire U.S. Arctic Ocean in December 2009 to any commercial fishing.

31

with less sea ice to be small, but we are not aware of any study that has estimated the magnitude of

economic impacts from sea ice loss over any particular time frame.

Collapse of Ice Sheets. Melting of the Greenland Ice Sheet (GIS) and West Antarctic Ice Sheet (WAIS) are

also ranked relatively high among the list in Table 2 in their likelihood of occurring as a result of climate

change. Lenton (2011), for example, considers them to be medium and medium-high on a 5 category

scale and the integrity of both ice sheets are widely thought to be subject to tipping points. That is, most

ice-sheet models exhibit multiple stable states and nonlinear transitions from one to another. The

threshold for GIS collapse is generally thought to be accessible this century. IPCC (2007) put the

threshold at +1.9–4.6°C global warming (above preindustrial); Gregory et al. (2004) and Huybrechts and

De Wolde (1999) find the threshold to be around 3°C of regional warming. More recent assessments

suggest a closer and narrower range above present is possible because of the speed of recent changes

(e.g., +1-2°C global warming (Lenton et al. 2008)), and we may have already transgressed a threshold

beyond which the ice sheet retreats on to land (Lenton and Ciscar 2012), leading to about 1 m of global

sea-level rise (Ridley et al., 2009). The level of warming needed to trigger full WAIS collapse is generally

thought to be further off than for the GIS. Lenton et al (2008) assessed the WAIS threshold to be about

+3-5°C global warming (above preindustrial); this is consistent with historical evidence of repeated WAIS

collapse under this level of warming (Naish et al., 2009). Others specify the threshold in terms of ocean

temperature – e.g., when surrounding ocean warms by around 5°C (Pollard and DeConto, 2009).

The primary physical impacts of the melting of either the GIS and/or WAIS are large increases in sea

level and amplified warming. Estimates of the total sea level rise from the complete melting of the GIS

and WAIS are as high as 2-7 meters and 3-5 meters, respectively, but this full impact for either ice sheet

is not expected to be realized for at least 300 years after the threshold is past (Lenton et al. 2008). In the

very long run there is a significant probability of total sea level rise greater than 10 m (Lenton and Ciscar

2012), but projections for this century are much more modest. The IPCC (2007) estimated total climate

change induced global sea level rise to be in the range of +0.4-0.7m in 2100 from pre-industrial times,

with most of the increase due to thermal expansion rather than ice sheet loss. More recent assessments

suggest a somewhat wider range is possible. NAS (2011) estimates global sea level rise in 2100 to be in

the range of 0.5 – 1 m, with GIS and WAIS melting contributing up to 0.285m (under the IPCC A1B

emissions scenario, or +2.3-4.3°C warming, assuming a doubling in ice discharge for the Greenland

outlet glaciers, and the Amundsen Coast Basin in Antarctica). The rest would come from melting of

glaciers and ice caps (0.37±0.02 m), and thermal expansion (0.23±0.09 m). Nicholls et al. (2011) contend

32

a pragmatic range for overall global sea level rise from all sources is 0.5-2m by 2100 (relative to 1980-

99). The low end of this range is based on AR4 projections and that observed sea level rise is closer to

the high end of SRES scenarios (Rahmstorf et al. 2007; Pielke 2008). The high end of Nicholls et al.’s

range is based on post-AR4 studies (summarized in Table 2 of Nicholls et al. 2011). Finally, although they

do not assign specific probabilities, the authors suggest the upper part of the 0.5 - 2m range is unlikely

to be realized. It should also be noted that there remains a large spread in deviations of regional sea-

level rise from global mean value (Pardaens et al. 2010); gravitational adjustment will make sea level rise

smallest nearest the ice sheet that is being lost and greatest on the opposite side of the planet

(Mitrovica et al. 2009; Mitrovica et al. 2001).

Among the post-AR4 studies, Pfeffer et al. (2008) avoid model simulations and instead estimate the

maximum contribution of ice sheet collapse to global sea-level rise as constrained only by the maximum

ice speed possible and the width of ice discharge outlets. Using this type of physical constraint analysis

they conclude that global sea-level rise in excess of 2 meters is “physically untenable” by 2100, and find

Greenland contributes a maximum of 50 cm to this rise. Other studies find Greenland’s contribution to

be much smaller. For example, Lenton and Ciscar (2012) note that one state-of-the-art study estimates

only 4.5 cm sea-level rise from Greenland ice dynamics (Price et al. 2011). Moon et al. (2012) also find

estimates consistent with Price et al. They attribute this finding to slower glacier acceleration (based on

wide sampling of actual 2000 to 2010 changes) than Pfeffer et al.(2008). Melt water from Greenland

could have a small effect of weakening the Atlantic thermohaline circulation (Driesschaert et al. 2007;

Jungclaus et al. 2006). See Lenton and Ciscar (2012) for discussion of other longer run atmospheric and

ocean circulation impacts of losing the ice sheet.

Antarctica’s maximum contribution to global sea level rise has been estimated at around 60 cm this

century (Pfeffer et al. 2008), but Levermann et al. (2012) contend it could be higher because Pfeffer’s

assumptions are less suited for Antarctica - i.e., discharge is potentially quicker than Greenland due to

outlet glaciers being less constrained by topography. Lenton and Ciscar (2012) discuss other impacts

resulting from WAIS melt – e.g., encouraging retreat of the GIS, flooding of extensive regions of

permafrost in the Arctic, releasing methane and carbon dioxide.

The main economic impacts from sea level rise stem from damages associated with dryland and wetland

loss and infrastructure loss, or the adaptation and relocation costs associated with avoiding these losses.

Of the Earth system changes listed in Table2, sea level rise has received the most explicit representation

33

in integrated assessment models to date, and in at least one model, DICE2010, the contribution of

melting of the GIS and WAIS can be examined separately.22 Modeling of the resulting economic damages

is generally less detailed. DICE2010’s loss function includes a simple quadratic function of sea level rise.

PAGE09 models damages from sea level rise as increasing less than linearly with the sea level based on

the assumption that low-lying shore line regions suffer higher damages than inland regions.

The FUND model explicitly includes damages associated with the inundation of dry land due to sea level

rise. The amount of land lost within a region is dependent upon the proportion of the coastline being

protected by adequate sea walls and the amount of sea level rise. Nicholls et al (2008) adjust FUND 2.8n

to allow for nonlinear impacts from extreme sea level rise to estimate impact of collapse of WAIS. This

study advances the modeling of the welfare impacts of WAIS disintegration in that it includes the rate of

SLR and its interactions with adaptation measures. However, the review above indicates the abstract

WAIS melting scenarios considered by Nicholls et al.’s scenarios (e.g., an additional 5-m rise in 100 years

(by 2130)) are inconsistent with the timescales considered relevant for WAIS by the natural science

communities. Lenton and Ciscar (2012) also highlight the disconnect with the latest science on WAIS

melt. For example, they note that 1) the fastest WAIS collapse yet simulated by models takes around

1000 years (Pollard and DeConto, 2009), 2) the fraction of the WAIS vulnerable to abrupt collapse is

equivalent to around 3.3 m rather than 5 m of sea-level rise (Bamber et al., 2009), and 3) the sea-level

rise would not be globally uniform, but rather would be higher along U.S. eastern seaboard (Mitrovica et

al., 2009).

22 In DICE2010, the average global sea level anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland ice sheet, and 4) melting of the Antarctic ice sheet. The parameters governing these four components are calibrated to match consensus results from the IPCC’s Fourth Assessment Report. The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period and the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900. The contribution to sea level rise from melting of the Greenland ice sheet is more complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1 oC and increases linearly from 0 meters to a maximum of 7.3 meters for temperature anomalies between 1 °C and 3.5 °C. The contribution to SLR in each period is proportional to the difference between the previous period’s sea level anomaly and the equilibrium sea level anomaly, where the constant of proportionality is a quadratic function of the temperature anomaly in the current period. The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

34

Increase in amplitude and/or variability of ENSO. The El Nino/La Nina Southern Oscillation (ENSO) is a

periodic climate pattern characterized by variations in the temperature of the surface water of the

tropical eastern Pacific Ocean—warming (El Niño) or cooling (La Niña)—and air surface pressure in the

tropical western Pacific (the Southern Oscillation). The ocean warming and pressure variations are

generally coupled so that the warm oceanic phase, El Niño, accompanies high air surface pressure in the

western Pacific, while the cold phase, La Niña, accompanies low air surface pressure in the western

Pacific. Many of the mechanisms and physical feedbacks that control the characteristics of ENSO are

expected to be affected by rising GHG emissions. For example, expected mean changes over the tropical

Pacific include: a weakening of tropical easterly trade winds, faster warming in surface ocean

temperatures near the equator, shoaling (deepening) of the equatorial thermocline (the thin layer of

water that marks the rapid temperature transition between the wind-mixed upper ocean and deeper

layers), and steeper temperature gradients across the thermocline. Because the impacts of these

changes on the amplifying and damping processes could partly cancel each other out, most recent

studies conclude that it is not clear at this stage what the net ENSO response to climate change will be

and is thus highly uncertain. Although the first global circulation model studies showed a shift to more

persistent or frequent El Nino-like conditions, subsequent model intercomparisons and more recent

assessments find models are highly inconsistent with respect to their projections of change in ENSO

amplitude, frequency, and variability (see e.g., Guilyardi et al. 2009, Collins et al. 2010, Latif and

Keenlyside 2009). As summarized in Figure A2, some models show an increase in the amplitude of ENSO

variability in the future, others show a decrease, and some show no statistically significant changes

(Collins et al. 2010).

Figure A2. Projected Changes in the Amplitude of ENSO Variability*

35

* Figure taken directly from Collins et al. (2010), and show the projections from the various models included in the Coupled Model Intercomparison Project (CMIP3) (Meehl et al. 2007). The measure is derived from the interannual standard deviation of a mean sea-level-pressure index, which is related to the strength of the Southern Oscillation variations. Positive changes indicate an ENSO strengthening, and negative changes indicate a weakening. Statistical significance is assessed by the size of the blue bars, and the bold bars are those judged to have the best simulation of present-day ENSO characteristics and feedbacks.

The uncertainty in model predictions does not mean that strong changes will not happen and transient

ENSO responses are possible as well. For example, since the surface ocean changes faster than the deep

ocean, initial surface warming could lead to increased ENSO activity although in a longer run equilibrium

state, after GHG concentrations stabilize, ENSO may be more stable (Latif and Keenlyside 2009). Yeh et

al. (2009) find an increase in the occurrence of a different type of El Nino event (termed the Central

Pacific El Nino) under global warming, and expect this could lead to more effective forcing of drought

over India and Australia. Furthermore, Lenton et al. (2008) argue that ENSO impacts, even if smooth and

gradual, may exhibit tipping point behavior, with the transition time to a new state being on the order of

100 years. While Lenton et al. (2008) consider there to be a significant probability of a future increase in

ENSO amplitude that is accessible this century (+3-6 in global mean temperature, although the existence

and location of any threshold is particularly uncertain), in his most recent assessment Lenton (2011)

gives it a low likelihood of occurring relative to other climate tipping points.

The impacts of changes in ENSO include, but are not limited to, increased regional rainfall, drought,

monsoons, and other natural disasters. In El Nino years, countries on the western side of South America,

like Peru and Chile, experience unusually heavy rainfall, while on the other side of the Pacific, parts of

36

Australia and Indonesia suffer from severe drought (Latif and Keenlyside 2009, Easterling et al. 2000).

Increased drought in Southeast Asia from an increase in ENSO climate variability (Lenton et al. 2008)

could also have an impact on India’s monsoon season (Kumar et al. 2006). Impacts of changes in ENSO

variability would not be limited to countries adjacent to the Pacific, however. In North America, El Nino

phases tend to produce much more rain in Southern California, higher number of frost days in Florida,

and lower hurricane activity in the North Atlantic (Latif and Keenlyside 2009). Several studies have also

investigated the potential role that changes in ENSO patterns play in the salinity of the Atlantic and

hence the stability of the THC (e.g., Latif et al. 2000, Thorpe et al. 2001, Mignot and Frankignoul 2005).

Finally, studies that have examined the ecological impacts of El Nino have found evolutionary impacts on

finch beak size in the Galapagos (Boag and Grant 1984), changes in marine biotic systems (Roemmich

and McGowan 1995, Sagarin et al 1999), and associations with widespread coral bleaching events

following intense El Nino periods (Coffroth et al 1990, Glynn 1990).

The economic implications of potential changes in ENSO variability or amplitude would likely include

agricultural and health impacts and hurricane related damages (death, injury, property damage) in many

regions. However, given underlying uncertainty with regard to physical impacts described above, we

would expect there is also substantial uncertainty with regard to the timing and magnitude of the

economic impacts. Increases in crop losses from unusually heavy rain or drought and storm damages

may be due not only to increased strength or frequency of natural disasters but also due to diminished

forecasting ability which can impair preparedness plans. Many studies have examined the damages of

various extreme weather events on agriculture, including several that estimate the value of farmers

adapting to ENSO event information (e.g., Solow et al. 1998, Chen and McCarl 2000) or the economic

damages to U.S. agriculture from ENSO events (e.g., Adams et al. 1999). However, the only study we

have found that estimates the economic consequences of shifts in ENSO frequency or strength is by

Chen et al. (2001). Chen et al. (2001) use a stochastic model (which simulates production, acreage

allocation and consumption based on a stationary joint probability distribution of yields for 10 crops in

63 U.S. and 28 world regions from 1972 to 1993) to estimate annual damages to agriculture from two

changes in ENSO patterns as predicted by Timmermann et al. (1999) – i.e., a 40% increase in ENSO

frequency and a 10% increase in intensity (the frequency of the stronger El Niño and La Niña events).

Their analysis also indicates farmers would be able to mitigate some damages if ENSO forecasts were

available to help them anticipate events and alter planting decisions to smooth out the impacts of ENSO

37

phases. See Meza et al. (2008) for a recent survey of published evidence about the economic value of

seasonal climate forecasts for agriculture.

Studies have identified relationships between ENSO and numerous health impacts – e.g., the incidence

of malaria in South America, rift valley fever in east Africa, dengue fever in Thailand, hantavirus

pulmonary syndrome in the southwestern U.S., childhood diarrhoeal disease in Peru and cholera in

Bangladesh. However, overall the regions expected to be the most vulnerable to health risks from a

potential increase in amplitude of ENSO variability are areas around the Pacific and Indian oceans (Patz

et al. 2005). Finally, there are several studies that examine the economic impacts of intensification of

hurricanes and cyclones due to global warming (e.g., Nordhaus 2010, Narita et al. 2009, Narita et al

2010, and for earlier estimates, Pielke et al. 2001). These generally model damages as a function of wind

speed and storm frequency,23 but none to our knowledge have made a specific link to changes in ENSO

variability or amplitude.

Dieback of Forests: Amazon Rainforest and Boreal Forest.

Amazon Rainforest. Climate change induced dieback of the Amazon rainforest is generally

thought to be due to widespread reductions in precipitation and lengthening of the dry season, primarily

due to more persistent El Nino conditions (Cox et al. 2000, Cox et al. 2004, Betts et al. 2004).24 Regional

surface warming caused by substantial forest loss, along with land use change and increased fire

frequency amplified by forest fragmentation, will likely make it difficult for the forest to reestablish, thus

the system is thought to subject to a tipping point (Lenton et al. 2008) Land-use change alone could

potentially bring forest cover to a critical threshold. Lenton et al. (2008) assess the global mean

temperature change corresponding to a critical value of control to be about 3-4°C global warming

(consistent with Betts et al. 2004, White et al. 1999) and suggest a medium transition time of about 50

years to a new state. Jones et al. (2009) find the forest will be committed to a significant degree of

dieback before any is even observed and show longer timescales of gradual, rather than sudden, forest

23 For example, Narita et al. (2009) develop a separate climate impact module in FUND (FUND version 3.4) to estimate damages from tropical cyclones. In this module, damages are assumed to be proportional to the third power of wind speed (consistent with Emanuel 2005), and wind speed is assumed to increase by 4% per degree Celsius warming of tropical sea surface temperature (per the consensus statement by the World Meteorological Organization (WMO 2006)). Nordhaus (2010) finds a much higher value (namely 9) for the parameter representing the relationship between damages and maximum wind speed based on his statistical analysis of U.S hurricane impacts. 24 Changes in the gradient of the Atlantic sea surface temperatures between the Northern and Southern hemispheres are also thought to play a role (Harris et al. 2008).

38

loss as temperatures exceed about 3°C global warming. Others specify the threshold in terms of the

extent of forest loss – e.g., a tipping point may be transgressed once deforestation exceeds about 40%

of the entire Amazon basin (Davidson et al. 2012, Nobre and Borma 2009). Lenton (2011) assigns a

“medium” likelihood of massive Amazon dieback and its impacts relative to other climate tipping points,

but as with other events, scientists are generally a long way from providing precise estimates of the

probability of this tipping point occurring (Cox et al. 2004).

Although the location of a tipping point for the entire Amazon basin is likely to continue to be difficult to

define for some time to come, some large-scale changes have already been observed in parts of the

Amazon basin, such as lengthening of the dry season (Butt et al. 2011, Knox et al. 2011) and increases in

wet season river discharge in some ecologically and agriculturally important areas (Costa et al. 2003, Coe

et al. 2011). Most studies predict these trends will continue and result in some dieback, but the intensity

and even the direction of the change are uncertain due to the differences in rainfall projections as well

as uncertainty of long-term CO2 fertilization effects. Generally, models predicting greater reductions in

precipitation forecast larger amounts of dieback (Cook and Vizy 2008) while global climate models (Li et

al. 2006) projecting smaller reductions (or increases) of precipitation do not produce dieback (Schaphoff

et al. 2006). Using the Hadley Centre global climate model, Cox et al. (2000) find that CO2 fertilization

helps to maintain the rainforest cover through about 2050, but the warming and drying eventually lead

to abrupt reductions in the forest fraction (e.g., 78% loss in vegetation carbon and a 72% loss in soil

carbon by the 2090s). The threshold for abrupt reductions is highly uncertain and the rate and extent of

loss is model dependent. However, Cox et al. (2000, 2004) find Amazon dieback to dominate global

vegetation carbon loss projections once climate effects are incorporated into the carbon cycle.

Huntingford et al. (2008) explore uncertainties in Cox et al. (2000) predictions of Amazon dieback and

find that the predicted 21st century loss of Amazonian rainforest is robust across a wide range of global

climate sensitivities, as well as with more sophisticated modeling of photosynthetic behavior coupled

with soil moisture stress and the introduction of a dynamic vegetation model. However, others point out

that the threshold for abrupt reductions is highly uncertain and the rate and extent of forest loss is

model dependent. Willis and Bhagwat (2009) caution that improved characterization of topography or

“microclimatic buffering” and full acclimation capacity of plants and animals can seriously alter model

predictions. Lapola et al. (2009) developed a new vegetation model for tropical South America and

found that when the CO2 fertilization effects are considered, they overwhelm the impacts arising from

temperature. In this case, rather than the large-scale dieback predicted by Huntingford et al. (2008),

39

tropical rainforest biomes remain the same or substituted by wetter and more productive biomes.

However, for 2 of the 14 models, this result was dependent on the dry season not extending beyond 4

months; if it does, then the tropical biome becomes savanna. Malhi et al. (2009) found rainfall regime of

E. Amazonia is likely to shift over the 21st century in a direction that favors more seasonal forests rather

than savanna, and that rainforest-favoring climate remaining will likely remain in W. Amazonia (although

the drier northern and southern margins may not), with 10% possibility of shifting from a generally

aseasonal moisture regime to a seasonally dry regime.

Rammig et al. (2010) develop formal characterization of uncertainty around rainfall projections and

estimate probability density functions for a change in vegetation carbon storage (kg C m-2) by Amazon

region for 2070 –2100 vs 1970–2000 on the basis of rainfall projections from 24 GCMs (under IPCC A1b

warming scenario) that are weighted by climate model performance for current conditions. They also

perform sensitivity analysis over the strength of the CO2 fertilization effect. They find biomass

vulnerability to vary across regions, with the CO2 fertilization being a key source of uncertainty. As

summarized in Table A1, under weak CO2 fertilization (i.e., no additional CO2 fertilization effects

compared with current conditions), Eastern and Northwestern Amazonia are likely to see small biomass

loss, Southern Amazonia will experience the largest biomass changes, and Northeastern and Southern

Brazil are less vulnerable to further drying. The probability of simulated forest dieback due to decreased

rainfall is greatly reduced when a strong CO2 fertilization response is added to the model (Rammig et al

2010),

Table A1. Projected probability of biomass loss in five regions of South America* Probability of any

biomass loss (%) Probability of biomass loss of25% or more

CLIM only CLIM+ CO2

CLIM only CLIM+ CO2

Eastern Amazonia 86.40 0.15 15.70 0.00 Northwestern Amazonia 85.90 0.00 1.10 0.00 Southern Amazonia 100.00 0.00 61.30 0.00 Northeastern Brazil 47.30 0.00 1.00 0.00 Southern Brazil 27.03 0.00 0.90 0.00

* Table taken from Rammig et al. (2010). Biomass loss is expressed by a reduction in the vegetation carbon storage or ‘biomass’ (in kg C m-2). The CLIM+CO2 case assumes standard CO2 fertilization effects in addition to climate change, including a reduced transpiration rate and higher amount of photosynthesis; the CLIM only case assumes no additional CO2 fertilization effects compared with current conditions.

40

In addition to the loss of the forest cover itself, key physical impacts of Amazon rainforest dieback

include biodiversity impacts and additional reductions in precipitation (~20-30%, Zeng et al. 1996).

Dieback could also have an amplifying effect to climate change (Kleidon and Heimann 2000) with the

forest eventually becoming a CO2 source, which could ultimately release up to ~100 Gt C (Allison et al.

2009). See Davidson et al. (2012) for thorough review of major factors and linkages affecting the

transition of the Amazon from a carbon sink to a carbon source.

Boreal Forest. The Boreal Forest is an immense span of forests, lakes, wetlands, rivers, and

tundra covering approximately 6.5 million square miles in northern regions of Russia, Scandinavia,

Canada and Alaska. The region has a biologically rich and largely unspoiled ecosystem; it is home to wide

variety of tree species and wildlife, and billions of birds breed there each spring. Cold temperatures

prevent plant remains from decomposing and allow the region to remain an important global carbon

sink. The area is projected to be vulnerable to rising temperatures and other impacts of climate change,

with increased water stress and increased peak summer heat stress leading the trees to be more

vulnerable to pests, disease, mortality, and fires, along with decreased reproduction rates.

The forest could experience extensive dieback (Lucht et al. 2006, Joos et al 2001), and be replaced by

open woodlands or grasslands (Hogg and Schwarz 1997) that support increased fire frequency, amplify

summer warming, and potentially produce a strong positive feedback. Studies have already reported

widespread pest induced tree mortality (such as the Canadian mountain pine beetle invasion (Kurz et al.

2008a)) and an overall decline in boreal forest area due to increasing heat stress (Lucht et al. 2006, Joos

et al 2001). Lenton et al. (2008) suggest a threshold for large-scale dieback of 3-5°C global warming, but

limitations in existing models and physiological understanding make this highly uncertain. Kurz et al.

(2008b) find that Canada’s forests have already turned from a carbon sink to a carbon source. Others,

however, argue that in contrast to the amplifying effect of Amazon dieback, a diminishing global climate

feedback effect could accompany boreal forest dieback (Allison et al 2009). Dieback would release CO2

but this would be outweighed by cooling if more of the snow cover was exposed (Betts 2000). Finally,

since tree growth in this region is generally more limited by temperature than precipitation, studies

generally find that at lower levels of warming, climate change will tend to increase boreal forest growth

(e.g., Garcia-Lopez and Allue 2012, Aaheim et al. 2011) and lead to a northward expansion of the forest

rather than dieback (e.g., Jones et al. 2009, Cramer et al. 2001, Scholze et al. 2006). Overall, Lenton

(2011) assigns a low likelihood of transgressing a tipping point for boreal forest dieback relative to other

large-scale Earth system changes and assess medium-low impacts relative to other climate tipping

41

points. In our review, we did not find any projections of the rate and extent of dieback over 21st century,

or associated amplified warming, similar to projections for the Amazon.

Global integrated models so far find an overall positive impact of climate change up to a certain level of

global temperature increase, at least in boreal forests (Aaheim et al. 2011, Sohngen et al. 2010). This

stems primarily from the CO2 fertilization effect – e.g., Tol (2002) assumes a significant productivity gain

in boreal forests in FUND. To our knowledge, existing models of climate impacts of forests and forest

management have not been applied to the examination of economic consequences of large-scale

dieback of either the Amazon rainforest or boreal forests. However, forests are represented to varying

degrees in global, regional, and national studies of climate change impacts and forest management. See

Aaheim et al. (2011) for a thorough review of economic and ecological models that address impacts and

adaptation to climate in the forest sector. Sohngen et al.’s (2010) general overview of potential climate

change impacts on the forest sector in the short, medium, and long run highlights the need for fuller

integration of ecological and economic models (which tend to work on different time and geographic

scales, and neglect to take into account adaptation in examining ecological impacts) to better

understand how forest ecosystems and markets may be affected by climate change. That said, existing

IAMs may be further developed to improve representation of potential dieback and associated damages

at higher temperatures. And the importance of certain modeling improvements may vary by region. For

example, since boreal regions are less managed and touched by human influence (Sohngen et al. 2010),

detailed modeling of adaptation responses, timber market impacts, and interactions with agriculture

may play a smaller role at least in the nearer term. Better modeling of changes in precipitation patterns

(e.g., climate impacts on ENSO) may also be less important than in tropical rainforests.

Weakening/Shutdown of Ocean Circulations.

Atlantic Thermohaline Circulation (THC). The THC is an ocean water circulation pattern

responsible for a large fraction of northward heat transport of the Atlantic Ocean. The response of the

THC to climate change is generally thought to hinge on factors affecting the water density and pressure

gradients at high latitudes, especially the addition of freshwater into the North Atlantic from higher

precipitation and ice melt and the warming of surface waters from higher atmospheric temperatures.

The IPCC (2007) argued that an abrupt transition of the THC is ‘‘very unlikely’’ (probability less than 10%)

to occur before 2100 and that any transition is likely to take a century or more. However, the IPCC

projection did not reflect the impact of freshwater runoff from GIS melt. Subsequent simulations

42

suggested that a THC tipping point is accessible this century (Mikolajewicz et al 2007); expert elicitation

suggested a 50% probability of passing a threshold for THC collapse at 4°C global warming (Kriegler et al

2009). Some studies consider a weakening of the THC resulting from these changes to be much more

likely to occur than a complete shutdown (e.g., Stouffer et al. 2006, Zickfeld et al. 2007), although the

rate of warming is a critical factor (Schmittner and Stocker 1999, Naevdal and Oppenheimer 2007). For

example, the THC may be sustained under a 5°C temperature increase occurring over 500 years, but a

complete collapse could occur if the same increase occurred over 100 years (Naevdal and Oppenheimer

2007). Lenton and Ciscar’s (2012) review suggests the debate over the vulnerability of the THC is still far

from settled.

Even among models predicting an anthropogenic weakening of the THC, the impacts are not

expected to be imminent (Latif et al. 2006, Zhang et al. 2011). It is expected that the THC will remain

within the range of natural variability during the next several decades (Latif et al. 2006). Lenton et al.

(2008) assess the global mean temperature change corresponding to a critical value of control to be

about 3–5°C global warming and suggest a gradual transition time of about 100 years to a new state.

Many studies have focused primarily on the problems a collapse of the THC may pose to

countries bordering the North Atlantic. Northwest Europe could experience substantial cooling

(although underlying global warming trend still tends to dominate) (Gregory et al. 2005), reduced

rainfall (Vellinga and Wood 2002), and additional increase in sea level of approximately 25-50 cm

(Levermann et al. 2005; Vellinga and Wood 2008). Ecosystem impacts from a weakening/shutdown of

the THC, however, are not expected to be confined to the North Atlantic, due to changes not only in

temperature but also in precipitation patterns (Higgins and Vellinga 2004). The Northern Hemisphere is

likely to experience reduced rainfall while a shutdown induced shift of the Intertropical Convergence

Zone could lead to pronounced precipitation increases over South America and Africa.

Efforts to model the economic impacts of a THC shutdown or weakening include one analysis

using an extension of the DICE model (Mastrandrea and Schneider 2001), and a few using variations of

the FUND model (e.g., Link and Tol 2011). As discussed in Section 3, Mastrandrea and Schneider (2001)

model the THC related damages as a series of hypothetical nonlinear enhancements to the DICE damage

function where they assume damages range from 1-25% of global GDP for a complete shutdown and

0.5-12.5% loss in GDP from a weakening. Link and Tol (2011)’s analysis is somewhat more connected to

the scientific literature on the THC in that the authors base the average temperature change expected

43

from THC changes on Vellinga and Wood (2002). They use the FUND model to monetize damages in

2100 from temperature changes expected under a scenario in which a shutdown occurs with certainty

and the temperature transition between the two states of the THC occurs linearly over a 30 year period

(2070-2100).25 Although this is a more rapid shutdown than expected in many models, it is consistent

with paleo-data (Lenton and Ciscar 2012). Consistent with other studies using the FUND model, the key

economic sectors affected are water resources and energy consumption, as well as cardiovascular and

respiratory diseases among health impacts. Overall, the authors find that a complete THC shutdown has

a relatively small negative effect on global welfare (-0.1% of global GDP in 2100), but impacts vary

considerable across regions and some countries may be severely affected. National results are likely to

be biased to some unknown degree, however, due to the omission of several impact categories, such as

impacts on fisheries, tourism, precipitation, sea level rise, the distribution of extreme events in the

North Atlantic, and the probability of transgressing monsoon tipping points in other regions. Lenton and

Ciscar (2012) note that many of these omitted impacts are likely to be much more significant than those

experienced from temperature changes alone.

Antarctic Bottom Water (AABW) formation. The stability of the Antarctic Bottom Water

formation is another aspect of the great ocean conveyer belt that may be affected by climate change.

Similar to the mechanism affecting the THC, freshening of the water in the Southern Ocean (SO) could

cause the AABW to weaken (Seidov et al. 2005) or collapse (Bi et al. 2001), causing a cooling around

Antarctica. Lenton et al. (2008) do not consider the AABW to be subject to a tipping point since more

assessment is needed to establish the robustness of collapse and to assess the threshold at which it may

occur. Recent research stresses the importance of the state of the AMOC (present state active mode vs.

an off state) when analyzing the impact of a freshwater input in the Southern Ocean (Swingedouw et al.

2009). However, study of this potential climate “catastrophe” is still in its infancy. No studies have

attempted to incorporate a possible weakening or shutdown of the AABW in assessing the economic

consequences of climate change.

Collapse and/or Increased Volatility of Major Monsoon Seasons.

Collapse of West African monsoon (WAM)/Greening of the Sahel. The Sahel is an approximately

1000 km wide semi-arid belt across Northern Africa, bordered by the Sahara to the north and less arid

25 Link and Tol (2011) is an extension of Ceronsky et al. (2005) and Link and Tol (2004) with more detailed spatial resolution.

44

savanna regions to the South, that is irrigated by the West African monsoon (WAM) summer rains.

There is little to no consensus in 21st century precipitation change projections for West Africa (Cook and

Vizy 2006; Douville et al. 2006; Christensen et al. 2007; Giannini et al. 2008; Druyan 2011). However,

since the WAM circulation is affected by sea surface temperatures, any changes in the Atlantic THC

could also have implications for the WAM, and thus the Sahel region. According to one of the three IPCC

(2007) models that produces a realistic present climate for the Sahel, it is thought that a weakening of

the THC could contribute to warming in the Gulf of Guinea (Cook and Vizy 2006), disrupting the seasonal

onset of the WAM (Chang et al. 2008) and its subsequent movement northward into the Sahel (Hagos

and Cook 2007). In other words, there would be a southward shift in the WAM, which would further dry

the Sahel (Chang et al. 2008, Lenton and Ciscar 2012). However, among the other two realistic IPCC

models, one projects a wetter and greener Sahel (Cook and Vizy 2006; Patricola and Cook 2008), even if

the WAM collapses, and the other find no significant change in precipitation (Lenton and Ciscar 2012).

Lenton et al. (2008) assess that the global mean temperature change corresponding to a critical value of

control for WAM collapse and greening of the Sahel to be about 3–5°C global warming and suggest a

rapid transition time of about 10 years to a greener Sahel.

There are relatively few studies offering projections of the physical and economic impacts of changes to

the WAM and the Sahel region. The greening of the Sahel could increase the carrying capacity of the

region, with grasslands expanding into up to 45% of the Sahara, at a rate of up to 10% of Saharan area

per decade (Claussen et al 2003). Shrub vegetation is also thought to increase due to increased water

use efficiency under higher atmospheric CO2 (Lucht et al 2006). While these impacts seem to be

primarily regional, a diminishing feedback on global climate change could also result as the greening

would absorb CO2 and probably increase regional cloud cover (Allison et al. 2009). Given the significant

uncertainty about the direction of physical impacts, there has been little study or speculation about the

potential economic consequences of changes in the WAM, but a greener Sahel region is likely to include

some positive impacts to agricultural productivity.

Collapse/Volatile Indian Summer Monsoon (ISM). The Indian Summer Monsoon refers to the

rainy season that supplies the Indian sub-continent with about 80% of its annual rainfall. It is difficult to

isolate the effect of climate change on the ISM, or to identify potential tipping point behavior beyond a

critical threshold of warming, because the stability of the monsoon is thought to be likely already

disrupted by brown haze, or soot, that acts to cool the land surface (Ramanathan et al. 2005, Meehl et

al. 2008, Allison et al. 2009). Cooler land temperatures reduce the thermal contrast between land and

45

sea that is required to build up a monsoon, although increasing CO2 may counteract this effect to some

degree. The result may be increased volatility - where chaotic switches from active to weak monsoons

could occur from one year to the next (Lenton et al. 2008)-, more complex changes in the strength and

location of monsoons (Lenton and Ciscar 2012), or a potential collapse (Levermann et al 2009, Zickfield

et al 2005). In a recent review, Turner and Annamalai (2012) find that models show generally wetter

conditions over South Asia in the future, but model uncertainty remains high, especially regarding

interseasonal variability. Another player in interannual variations in monsoon rainfall is the ENSO.

Historically, above- (below-) average Indian monsoon rainfall has been generally associated with the

cold (warm) phase of the ENSO, although this relationship has become markedly less clear since the

1980s (Kumar et al. 1999). Ramanathan et al. (2005) find the brown haze forcing could lead to a

doubling of drought frequency within a decade.

In a country home to over a billion people and where 60% of agricultural production is rainfed, even

small changes in monsoon patterns can have large economic impacts for India. There may also be

tradeoffs or complementaries between impacts of brown haze aerosol emissions and greenhouse gases

that need to be taken into account. Both have been found to have contributed to reduced rice harvests

in India during the past two decades (Auffhammer et al. 2006). Since society can better plan and adapt

to mean interannual changes than increased variability or changes on shorter timescales, a better

understanding of the regional variation on subseasonal timescales will be key to assessing the most

serious consequences of changes in monsoon patterns on affected populations (Turner and Annamalai

2012). Even with a robust monsoon, changes in the intensity of extreme events and the duration of

drought spells could have devastating consequences. Several studies have found a decrease in the

frequency of moderate-to-heavy rainfall events over most parts of India (e.g., Dash et al. 2009;

Guhathakurtha et al. 2010) and a significant rise in the frequency and duration of monsoon breaks over

India during recent decades (see Ramesh Kumar et al. 2009, Turner and Hannachi 2010). However an

increase in frequency of extreme rainfall events (10 cm/day) has also been observed in some regions

(Goswami et al. 2006; Guhathakurtha et al. 2011). Turner and Slingo (2009) find an intensification of

both active and break events, although no change to the duration or likelihood of monsoon breaks.

Retreat of Tundra and Permafrost Thaw.

Tundra generally refers to a biome where the tree growth is hindered by low temperatures and

short growing seasons. The soils in these regions, especially in the Arctic tundra, is mainly permafrost, or

46

permanently frozen ground.26 Global warming impacts on the tundra (and other permafrost regions)

are of significant concern because they could lead to potentially large amplified warming feedback

effects. First, rising temperatures could allow the northern boundary of the boreal forest to encroach on

the tundra areas (specifically when regions exceed 1000 growing degree days above zero), leading to

amplified warming as the trees obscure the snow. Second, the permafrost could thaw. Thawing of

permafrost is of significant concern because this frozen soil holds vast amounts of carbon (e.g., 60 to

190 Pg of carbon frozen in arctic tundra soils alone and 20–60% of global soil carbon stores thought to

be in soils of boreal forests and northward (Hobbie et al. 2000); ~1466 Gt C in permafrost (Tarnocai et al.

2009).27 As the ground thaws, the carbon may be activated leading to enhanced methane and/or

carbon dioxide emissions.

There is evidence to suggest both of these feedback effects are occurring. Summer warming

trends are increasing shrub growth in the tundra (Chapin et al. 2005) and greening of the boreal forest

(Lucht et al. 2002). Permafrost temperatures have risen by up to 2 °C, particularly in colder areas. The

depth of soil above the permafrost that seasonally thaws each year has increased in Scandinavia, Arctic

Russia west of the Urals, and inland Alaska. The southern limit of the permafrost retreated northward by

30 to 80 km in Russia between 1970 and 2005, and by 130 km during the past 50 years in Quebec (ACIA

2004). Emission increases accompanying permafrost thaw have also been reported – e.g., a 22–66%

increase in methane emissions in Sweden (Christensen et al., 2004), a 10- fold increase in carbon dioxide

in a boreal forest (Goulden et al., 1998).

Lenton et al. (2008) does not consider either the retreat of tundra or permafrost thaw to be

climate tipping points since they do not exhibit nonlinear or threshold behavior. Models suggest the

transition from tundra to boreal forest will be a continuous process (Schaphoff et al 2006, Lucht et al

2006, Joos et al 2001) and future projections of permafrost thaw, although substantial, are quasi-linear

and cannot convincingly demonstrate threshold behavior (Stendal and Christensen 2002, Lawrence and

Slater 2005). However, global circulation models are just beginning to represent simple permafrost

dynamics, so the debate over the degradation rate, the stability of deeper permafrost, and magnitude of

feedback effects continues (e.g., Lawrence and Slater 2005,Delisle 2007,Burn and Nelson 2006, Froese

et al. 2008, Zimov 2009). Projections vary considerably on the exact amount of permafrost thaw under

26 Technical definition of permafrost is ground that is at or below 0C for at least two consecutive years. 27 Abrupt reductions in Arctic summer sea ice extent also help to increase rapid warming on land and subsequent permafrost degradation (Lawrence et al. 2008b).

47

various climate scenarios, but models agree that the extent of permafrost will decrease and the active

layer28 will deepen (e.g., Lawrence et al. 2008a). See Table A2 for a comparison of 2100 projections

across studies and IPCC scenarios (IPCC 2007).

Table A2. Published projections of permafrost degradation in 2100*

Study Scenario(s) Domain Decrease in permafrost area (%)

Increase in active layer depth (ALT) (cm)

Marchenko et al. (2008) A1B Alaska 7.0** 162*** Zhang et al. (2008a) B2, A2 Canada 16-20** 30-70 Zhang et al. (2008b) B2, A2 Canada 21-24 30-80 Euskirchen et al. (2006) A1B Northern

Hemisphere 27** -

Saito et al. (2007) A1B Northern Hemisphere

40-57 50-300

Lawrence and Slater (2005)

A2, B1 Northern Hemisphere

60-90 50-300

Eliseev et al. (2009) B1, A1B, A2 Northern Hemisphere

65-80** 100-200

Lawrence and Slater (2010)

A1B Northern Hemisphere

73-88 -

Lawrence et al. (2008a) A1B Northern Hemisphere

80-85 50-300

Schaefer et al. (2011) A1B Alaska 22-61 69-105 Schaefer et al. (2011) A1B Canada 22-36 55-90 Schaefer et al. (2011) A1B Northern

Hemisphere 20-39 56-92

*Adapted from Table 5 in Schaefer et al. (2011). **Calculated by Schaefer et al. (2011) from numbers or tables in text. ***Calculated by Schaefer et al. (2011) from estimated trends.

Some forecasts of the magnitude of the accompanying carbon feedback are also available. For example,

Schaefer et al (2011) forecast a cumulative permafrost carbon flux to the atmosphere of 190 ± 64 Gt C

by 2200. See Figure A3. They find that 46% of this release occurs after 2100, even though 80-90% of the

thawing occurs before 2100.

Figure A3. Projected cumulative carbon flux from permafrost degradation*

28 The active layer is the soil over the permafrost that freezes and thaws annually.

48

*Mean (± 1 standard deviation) projected cumulative permafrost carbon flux (Rpc) from degradation under various warming rates based on the A1B IPCC scenario. Figure taken directly from Schaefer et al. (2011).

Some economic studies mention the thawing of permafrost as a possible source of catastrophic or

abrupt climate change but the permafrost climate feedback is not represented in current IAMs

commonly used for policy analysis. A recent study by Lemoine and Traeger (2012) comes closest to

trying to model an impact specific to permafrost thaw and incorporates it as a fixed, instantaneous and

permanent doubling of climate sensitivity. Given the review above, it seems possible to at least

rudimentarily represent the additional carbon flux from thawing permafrost and associated damages

from additional warming in existing reduced form IAMs. More research and modeling is needed to

incorporate other damage categories – e.g., valuation of ecosystem impacts from permafrost thaw.

Other.

Massive release of marine methane hydrates. Similar to the frozen soils of the tundra, areas

under the sea floor store vast amounts of carbon. Between 500 and 10,000 GtC are thought to be stored

under the marine continental shelf and slope sediment in the form methane hydrates, a crystalline

structure of methane gas and water molecules (Brook et al. 2008). The concern is that as the oceans and

eventually the sediment layer warms under anthropogenic forcing, a massive release of the methane

could be triggered from these sea floors. Such a release would lead to significant amplified global

warming effects, since once methane release events begin, each one adds to the warning thus

promoting additional releases (Lenton et al. 2008). The timescale of the forcing needed for this to occur

is assessed to be over 1,000 years into the future because it will take that long for the sediment to warm

to the point of reaching the hydrate deposits (Lenton et al. 2008, Archer and Buffet 2005).

49

Ocean anoxia. Different mass extinctions of marine ecosystems have been linked to warming,

ocean acidification, and ocean anoxia – i.e., a complete depletion of oxygen below the surface levels. Of

these, ocean anoxia is most often discussed to potentially exhibit tipping point behavior under

anthropogenic forcing (Lenton et al. 2008). Ocean anoxic events occur when the ocean is completely

deleted of oxygen, causing mass extinctions. Anoxia is exacerbated by sustained phosphorus input to

the ocean (e.g., from human agricultural fertilizer application), and higher temperatures are expected to

further accelerate weathering processes which release phosphorus. Lenton et al. (2008) assess the long

response time of deep ocean phosphorus means that deep ocean anoxia will not occur for at least 1,000

years, but the potential for nearer term widespread coastal anoxia requires further study.

Climate-induced Arctic ozone hole. It is thought that a climate change-induced ozone hole could

form, especially over Europe (Austin et al 2003, Shindell et al 1998), as higher temperatures cool the

stratosphere that supports formation of ice clouds, which in turn provide a catalyst for stratospheric

ozone destruction (Lenton et al. 2008). Lenton et al. (2008) assess that the time needed for a qualitative

change to occur may be quite short (less than 1 yr), but there has been little study of the extent to which

global warming could trigger such an event, and the magnitude of the physical impacts.

50

References

Aaheim, Asbjørn, Rajiv Kumar Chaturvedi, and Anitha D. Sagadevan. 2011. Integrated modelling

approaches to analysis of climate change impacts on forests and forest management. Mitig Adapt

Strateg Glob Change 16: 247–266.

ACIA. 2004. Impacts of a Warming Arctic: Arctic Climate Impact Assessment. Cambridge University Press,

Cambridge.

Adams, Richard M., Chi-Chung Chen, Bruce A. McCarl, and Rodney F. Weiher. 1999. The economic

consequences of ENSO events for agriculture. Climate Research 13: 165–172.

Alley, R., J. Marotzke, W. Nordhaus, J. Overpeck, D. Peteet, R. Pielke, R. Pierrehumbert, and P. Rhines.

2003. Abrupt Climate Change. Science 299: 2005-2010.

Allison, I., N.L. Bindoff, R.A. Bindschadler, P.M. Cox, N. de Noblet, M.H. England, J.E. Francis, N. Gruber,

A.M. Haywood, D.J. Karoly, G. Kaser, C. Le Quéré, T.M. Lenton, M.E. Mann, B.I. McNeil, A.J. Pitman, S.

Rahmstorf, E. Rignot, H.J. Schellnhuber, S.H. Schneider, S.C. Sherwood, R.C.J. Somerville, K. Steffen, E.J.

Steig, M. Visbeck, A.J. Weaver. 2009. The Copenhagen Diagnosis, 2009: Updating the World on the

Latest Climate Science. The University of New South Wales Climate Change Research Centre (CCRC),

Sydney, Australia.

Anthoff D, C. Hepburn, and R. Tol. 2009. Equity Weighting and the Marginal Damage Costs of Climate

Change. Ecological Economics 68: 836-849.

Anthoff, D., Nicholls, R.J., Tol, R.S.J., and Vafeidis, A.T. 2006. Global and regional exposure to large rises

in sea-level: a sensitivity analysis. Working Paper 96, Tyndall Centre for Climate Change Research,

Norwich, UK.

Archer, D., and B. Buffett. 2005. Time-dependent response of the global ocean clathrate reservoir to

climatic and anthropogenic forcing. Geochemistry Geophysics Geosystems 6, Q03002

Arctic Monitoring and Assessment Program (AMAP). 2011. Snow, Water, Ice and Permaforst in the Arctic

(SWIPA): An Arctic Council ‘Cryosphere Project’ in Cooperation with IASC, CliC and IPY.

http://www.amap.no/swipa/SWIPA2011ExecutiveSummaryV2.pdf.

51

Auffhammer, Maximilian , V. Ramanathan, and Jeffrey R. Vincent. 2006. Integrated model shows that

atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proceedings of the

National Academy of Sciences 103(52): 19668–19672.

Austin, J., D. Shindell, S.R. Beagley, C. Brühl, M. Dameris, E. Manzini, T. Nagashima, P. Newman, S.

Pawson, G. Pitari, E. Rozanov, C. Schnadt, and T.G. Shepherd. 2003. Uncertainties and assessments of

chemistry-climate models of the stratosphere. Atmos. Chem. Phys. 3: 1-27.

Bamber, J.L., R.E.M. Riva, B.L.A. Vermeersen, and A.M. LeBrocq. 2009. Reassessment of the Potential

Sea-Level Rise from a Collapse of the West Antarctic Ice Sheet. Science 324: 901-903.

Baranzini, A. and Chesney, M. and Morisset, J. 2003. The impact of possible climate catastrophes on

global warming policy. Energy Policy 31(8):691-701.

Barnosky, Anthony D., Elizabeth A. Hadly, Jordi Bascompte, Eric L. Berlow, James H. Brown, Mikael

Fortelius, Wayne M. Getz, JohnHarte, Alan Hastings, Pablo A. Marquet, Neo D. Martinez, Arne Mooers,

Peter Roopnarine, Geerat Vermeij, John W. Williams, Rosemary Gillespie, Justin Kitzes, Charles Marshall,

Nicholas Matzke, David P. Mindell, Eloy Revilla, and Adam B. Smith. 2012. Approaching a state shift in

Earth’s biosphere. Nature 486: 52-58.

Betts R.A., P.M. Cox, M. Collins, P.P. Harris, C. Huntingford, and C.D. Jones. 2004. The role of ecosystem-

atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global

climate warming. Theoretical and Applied Climatology 78:157–175.

Betts, R. A. 2000. Offset of the potential carbon sink from boreal afforestation by decreases in surface

albedo. Nature 408: 187-190.

Bi, D.H., W.F. Budd, A.C. Hirst, and X.R. Wu, 2001: Collapse and reorganisation of the Southern Ocean

overturning under global warming in a coupled model. Geophysical Research Letters 28: 3927–3930.

Boag, PT, and PR Grant. 1984. The classical case of character release: Darwin’s finches (Geospiza) on Isla

Daphne Major, Galapagos. Biological Journal of the Linnean Society 22(3): 243-287.

Bosello, F. and Moretto M. 1999. Dynamic Uncertainty and Global Warming Risk. FEEM working paper,

number 80.99.

52

Brook, E., D. Archer, E. Dlugokencky, S. Frolking, D. Lawrence. 2008. Chapter 5. Potential for abrupt

changes in atmospheric methane. In: U.S. Climate Change Science Program Synthesis and Assessment

Product 3.4: Abrupt Climate Change.

Budikova, D. 2009. Role of Arctic sea ice in global atmospheric circulation: A review. Global Planet.

Change 68(3): 149–163.

Burn, C.R., and F.E. Nelson. 2006. Comment on “A projection of severe nearsurface permafrost

degradation during the 21st century” by David M. Lawrence and Andrew G. Slater. Geophysical Research

Letters 33: L21503.

Butt, N., Oliveira, P. A.& Costa, M. H. 2011. Evidence that deforestation affects the onset of the rainy

season in Rondonia, Brazil. J. Geophys. Res 116, D11120.

Cai Y, Judd KL, and Lontzek, S. 2012. The social cost of abrupt climate change. Working paper.

Calvin KV, JA Edmonds, B Bond-Lamberty, LE Clarke, SH Kim, GP Kyle, SJ Smith, AM Thomson, MA Wise.

2009. 2.6: Limiting Climate Change to 450 ppm CO2 Equivalent in the 21st Century. Energy Economics

31(2):S107-S120.

Castelnuovo, E. and Moretto, M. and Vergalli, S. 2003. Global warming, uncertainty and endogenous

technical change. Environmental Modeling and Assessment 8(4):291-301.

Ceronsky M, Anthoff D, Hepburn CJ, Tol RSJ. 2011. Checking the price tag on catastrophe: the social cost

of carbon under non-linear climate response. Working paper No. 392, ESRI.

Ceronsky, M., D. Anthoff, C.J. Hepburn, R.S.J. Tol. 2005. Checking the price tag on catastrophe: the social

cost of carbon under non-linear climate response. Working paper FNU-87, Research Unit Sustainability

and Global Change, Hamburg University.

Chang, Ping, Rong Zhang, Wilco Hazeleger, Caihong Wen, Xiuquan Wan, Link Ji, Reindert J. Haarsma,

Wim-Paul Breugem, and Howard Seidel. 2008. Oceanic link between abrupt changes in the North

Atlantic Ocean and the African monsoon. Nature Geoscience 1: 444-448.

Chapin, FS, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH,

Schimel JP, Beringer J, Chapman WL, Epstein HE, Euskirchen ES, Hinzman LD, Jia G, Ping CL, Tape KD,

53

Thompson CDC, Walker DA, Welker JM. 2005. Role of land-surface changes in Arctic summer warming.

Science 310: 657-660.

Chen, Chi-Chung, Bruce A. McCarl, and Richard M. Adams. 2001. Economic Implications of Potential

ENSO Frequency and Strength Shifts. Climatic Change 49: 147–159.

Chen, C. C. and McCarl, B. A. 2000. The Value of ENSO Information to Agriculture: Considerations of

Event Strength and Trade. Journal of Agricultural and Resource Economics 25: 368–385.

Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon,W.-T.,

Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr, A., Whetton, P.

2007. Regional climate projections. Climate Change 2007: the physical science basis. In: Solomon, S.,

Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Contribution of

Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,

pp. 847–940.

Christensen, T.R., T. Johansson, H.J. Åkerman, M. Mastepanov, T. Firborg, P. Crill, and B.H. Svensson.

2004. Thawing sub-arctic permafrost: Effects on vegetation and methane emissions. Geophysical

Research Letters 31: L04501.

Clark, P.U., N.G. Pisias, T.F. Stocker, and A.J. Weaver. 2002. The Role of the Thermohaline Circulation in

Abrupt Climate Change. Nature 415: 863-869.

Claussen, M., V. Brovkin, A. Ganopolski, C. Kubatzki, and V. Petoukhov. 2003. Climate change in

Northern Africa: the past is not the future. Climatic Change 57:99-118.

Coe, M. T., Latrubesse, E. M., Ferreira, M. E. & Amsler, M. L. 2011. The effects of deforestation and

climate variability on the streamflow of the Araguaia River, Brazil. Biogeochemistry 105: 119–131.

Coffroth, MA, HR Lasker, and JK Oliver. 1990. Coral mortality outside of the eastern Pacific during 1982-

1983; relationship to El Nino. In: Glynn PW (ed) Global ecological consequences of the 1982-83 El Nino-

Southern Oscillation. Elsevier, Amsterdam, pp. 141-182.

Collins, M, Soon-Il An, Wenju Cai, Alexandre Ganachaud, Eric Guilyardi, Fei-Fei Jin, Markus Jochum,

Matthieu Lengaigne, Scott Power, Axel Timmermann, Gabe Vecchi & Andrew Wittenberg. 2010. The

54

impact of global warming on the tropical Pacific Ocean and El Nino. Nature Geoscience 3: 391-397.

Cook, K.H., and E.K. Vizy. 2008. Effects of 21st c. climate change on the Amazon rain forest. Journal of

Climate 21: 542-560.

Cook, K.H., and E.K. Vizy. 2006. Coupled model simulations of the West African monsoon system: 20th

century simulations and 21st century predictions. J. Climate 19: 3681–3703.

Costa, M. H., Botta, A. and Cardille, J. A. 2003. Effects of large-scale changes in land cover on the

discharge of the Tocantins River, Southeastern Amazonia. J. Hydrol 283: 206–217.

Cox, P.M., R.A. Betts, M. Collins, P.P. Harris, C. Huntingford, and C.D. Jones. 2004. Amazonian forest

dieback under climate-carbon cycle projections for the 21st century. Theoretical and Applied

Climatology 78: 137–156.

Cox, PM, RA Betts, CD Jones, SA Spall, and IJ Totterdell. 2000. Acceleration of global warming due to

carbon-cycle feedbacks in a coupled climate model. Nature 408: 184–187.

Cramer, W., A. Bondeau, F. Ian Woodward, I.C. Prentice. R. Betts, V. Brovkin., P.M. Cox, V. Fisher, J.A.

Foley, A.D. Friend, C. Kucharik, M.R. Lomas, N. Ramankutty, S. Sitch, B. Smith, A. White, and C. Young-

Molling. 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate

change: results from six dynamic global vegetation models. Global Change Biology 7: 357-373.

Cropper, M. 1976. Regulating activities with catastrophic environmental effects. Journal of

Environmental Economics and Management 3(1): 1–15.

Dash, S. K., M.A. Kulkarni, U.C. Mohanty, and K. Prasad. Changes in the characteristics of rain events in

India. J. Geophys. Res. Atmos. 114, D10109.

Davidson, Eric A., Alessandro C. de Arau´jo, Paulo Artaxo, Jennifer K. Balch, I. Foster Brown, Mercedes M.

C. Bustamante, Michael T. Coe, Ruth S. DeFries, Michael Keller, Marcos Longo, J. William Munger, Wilfrid

Schroeder, Britaldo S. Soares-Filho, Carlos M. Souza Jr, and Steven C. Wofsy. 2012. The Amazon basin in

transition. Nature 481: 321-328.

Delisle, G. 2007. Near-surface permafrost degradation: How severe during the 21st century?

Geophysical Research Letters 34, L09503.

55

Deser, Clara, Robert Tomas, Michael Alexander, and David Lawrence. 2010. The Seasonal Atmospheric

Response to Projected Arctic Sea Ice Loss in the Late Twenty-First Century. Journal of Climate 23: 333-

350.

Douville, H., D. Salas-Melia, and S. Tyteca. 2006. On the tropical origin of uncertainties in the global land

precipitation response to global warming. Clim. Dyn. 26: 367–385.

Driesschaert, E., Fichefet, T., Goosse, H., Huybrechts, P., Janssens, I., Mouchet, A., Munhoven, G.,

Brovkin, V., and Weber, S. L. 2007. Modeling the influence of Greenland ice sheet melting on the Atlantic

meridional overturning circulation during the next millennia. Geophysical Research Letters 34, L10707.

Druyan, Leonard M. 2011. Studies of 21st-century precipitation trends over West Africa. International

Journal of Climatology 31(10): 1415-1424.

Easterling, D.A., GA Meehl, CA Parmesan, SA Changnon, TR Karl, and LO Mearns. 2000. Climate

Extremes: Observations, Modeling, and Impacts. Science 289(5487): 2068-2074.

Eliseev, A.V., M.M. Arzhanov, P.F. Demchenko, and I.I. Mokhov. 2009. Changes in climatic characteristics

of Northern Hemisphere extratropical land in the 21st century: assessments with the IAP RAS climate

model. Izvestiya Atmos. Ocean Phys. 45(3): 271–283.

Emanuel K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:

686–688.

Euskirchen E.S., A.D. McGuire, D.W. Kicklighter, Q. Zhuang, J.S. Clein, and co-authors. 2006. Importance

of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon

sequestration in terrestrial high-latitude ecosystems. Global Change Biol. 12(4): 731–750.

Fisher, A.C., and U. Narain. 2003. Global Warming, endogenous risk, and irreversibility. Environmental

Resource Economics 25(4):395-416.

Francis, J.A., W. Chan, D.J. Leathers, J.R. Miller, and D.E. Veron. 2009. Winter Northern Hemisphere

weather patterns remember summer Arctic sea-ice extent. Geophys. Res. Lett. 36, L07503.

Froese, D. G., J. A. Westgate, A. V. Reyes, R. J. Enkin, and S. J. Preece. 2008. Ancient Permafrost and a

Future, Warmer Arctic. Science 321(5896): 1648.

56

Garcia-Lopez, J.M., and C. Allue. 2012. A phytoclimatic-based indicator for assessing the inherent

responsitivity of the European forests to climate change. Ecological Indicators 18: 73-81.

Giannini, Alessandra, Michela Biasutti, and Michel M. Verstraete. 2008. A climate model-based review of

drought in the Sahel: Desertification, the re-greening and climate change. Global and Planetary Change

64: 119-128.

Gjerde, J., Grepperud, S. & Kverndokk, S. 1999. Optimal climate policy under the possibility of a

catastrophe. Resource and Energy Economics 21(3-4): 289–317.

Glynn, P.W. 1990. Coral mortality and disturbances to coral reefs in the tropical Eastern Pacific, p. 55-

126. In P.W. Glynn (ed.). Global ecological consequences of the 1982-83 El Niño-Southern Oscillation.

Elsevier, Amsterdam, The Netherlands.

Goswami, B.N., V. Venugopal, D. Sengupta, M.S. Madhusoodanan, and P.K. Xavier. 2006. Increasing

trend of extreme rain events over India in a warming environment. Science 314: 1442-1445.

Goulden, M.L., S.C. Wofsy, J.W. Harden, S.E. Trumbore, P.M. Crill, S.T. Gower, T. Fries, B.C. Daube, S.-M.

Fan, D.J. Sutton, A. Bazzaz, and J.W. Munger. 1998. Sensitivity of boreal forest carbon balance to soil

thaw. Science 279: 214-217.

Gregory, J. M., K. W. Dixon, R. J. Stouffer, A. J. Weaver, E. Driesschaert, M. Eby, T. Fichefet, H. Hasumi, A. Hu, J. H. Jungclaus, I. V. Kamenkovich, A. Levermann, M. Montoya, S. Murakami, S. Nawrath, A. Oka, A. P. Sokolov, and R. B. Thorpe. 2005. A model intercomparison of changes in the Atlantic thermohaline circulation in response to increasing atmospheric CO2 concentration. Geophysical Research Letters 32, L12703.

Gregory, J. M., P. Huybrechts, and S. C. B. Raper. 2004. Climatology - Threatened loss of the Greenland

ice-sheet. Nature 428: 616.

Guhathakurtha, P., O.P. Sreejith, and P.A. Menon. 2011. Impact of climate change on extreme rainfall

events and flood risk in India. Journal of Earth System Science 120(3): 359-373.

Guilyardi, E. et al. 2009. Understanding El Niño in ocean–atmosphere General Circulation Models:

progress and challenges. Bull. Am. Meteorol. Soc. 90: 325–340.

Hagos, S.M., and K.H. Cook. 2007. Dynamics of the West African Monsoon Jump. Journal of Climate 20:

57

5264–5284.

Harris, P.P., C. Huntingford, and P.M. Cox. 2008. Amazon basin climate under global warming: the role of

the seasurface temperature. Phil. Trans. R. Soc. B 363.

Hendricks, Darryll. 1992. Optimal Policy Response to an Uncertain Threat: The Case of Global Warming.

Unpublished manuscript, Kennedy School of Government, Harvard University.

Higgins, PAT, and M Vellinga. 2004. Ecosystem responses to abrupt climate change: teleconnections,

scale and the hydrological cycle. Climatic Change 64: 127-142.

Hobbie, S.E., J.P. Schimel, S.E. Trumbore, and J.R. Randerson. 2000. A mechanistic understanding of

carbon storage and turnover in high- latitude soils. Global Change Biology 6: S196-S210.

Hogg, E.H., and A.G. Schwarz. 1997. Regeneration of planted conifers across climatic moisture gradients

on the Canadian prairies: implications for distribution and climate change. Journal of. Biogeography 24:

527-534.

Holland, M.M., C.M. Bitz, and B. Tremblay. 2006. Future abrupt reductions in the summer Arctic sea ice.

Geophysical Research Letters 33, L23503.

Holling, C. 1973. Resilience and Stability of Ecological Systems. Annual Review of Ecological Systems 4:1-

23.

Honda, M., J. Inoue, and S. Yamane. 2009. Influence of low Arctic sea-ice minima on anomalously cold

Eurasian winters. Geophys. Res. Lett. 36, L08707.

Hope, C. 2006. The marginal impacts of CO2, CH4, and SF6 emissions. Climate Policy 6: 537-544.

Hope, C. 2011. The Social Cost of CO2 from the PAGE09 Model. Economics. Discussion Paper No. 2011-

39.

Hulme, M. 2003. Abrupt climate change: can society cope? Philosophical Transactions of the Royal

Society of London. Series A: Mathematical, Physical and Engineering Sciences 361(1810): 2001-2021.

Huntingford, Chris, Rosie A Fisher, Lina Mercado, Ben B.B Booth, Stephen Sitch, Phil P Harris, Peter M

Cox, Chris D Jones, Richard A Betts, Yadvinder Malhi, Glen R Harris, Mat Collins, and Paul Moorcroft.

58

2008. Towards quantifying uncertainty in predictions of Amazon ‘dieback’. Philos Trans R Soc Lond B Biol

Sci. 363(1498): 1857-1864.

Huybrechts, Philippe, and Jan de Wolde. 1999. The Dynamic Response of the Greenland and Antarctic

Ice Sheets to Multiple-Century Climatic Warming. Journal of Climate 12: 2169-2188.

Intergovernmental Panel on Climate Change (IPCC). 1996. Climate Change 1995 - The Science of Climate

Change. Working Group I, Second Assessment Report.

Intergovernmental Panel on Climate Change (2007a). Global Climate Projections, in Working Group I

Report: The Physical Science Basis. Fourth Assessment Report. Chapter 10.

Intergovernmental Panel on Climate Change (2007b). A Report of Working Group I: Summary for

Policymakers. http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm.pdf.

Jones, Chris, Jason Lowe, Spencer Liddicoat, and Richard Betts. 2009. Committed terrestrial ecosystem

changes due to climate change. Nature Geoscience 2: 484-487.

Joos, F., Prentice, I. C., Sitch, S., Meyer, R., Hooss, G., Plattner, G.-K., Gerber, S., and Hasselmann, K.

2001. Global warming feedbacks on terrestrial carbon uptake under the Intergovernmental Panel on

Climate Change (IPCC) Emission Scenarios. Global Biogeochemical Cycles 15: 891-907.

Jungclaus, J.H., H. Haak, M. Esch, E. Roeckner, and J. Marotzke. 2006. Will Greenland melting halt the

thermohaline circulation? Geophys. Res. Lett. 33, L17708.

Kattsov, V.M., V.E. Ryabinin, J.E. Overland, M.C. Serreze, M. Visbeck, J.E. Walsh, W. Meier, and X. Zhang.

2010. Arctic sea-ice change: a grand challenge of climate science. Journal of Glaciology 56(200): 1115-

1121.

Keller, K., R. Tol, F. Toth, and G. Yohe. 2008. Abrupt Climate Change near the Poles. Climatic Change 91:

1-4.

Keller, K., K. Tan, F. Morel, and D. Bradford. 2000. Preserving the ocean circulation: Implications for

climate policy. Climatic change 47(1-2): 17–43.

Kerr, Richard A. 2009. Arctic Summer Sea Ice Could Vanish Soon But Not Suddenly. Science 323(5922):

59

1655.

Kleidon, A., and M. Heimann. 2000. Assessing the role of deep rooted vegetation in the climate system

with model simulations: Mechanism, comparison to observations and implications for Amazonian

deforestation. Climate Dynamics 16: 183-199.

Knox, R., G. Bisht, J. Wang, and R. L. Bras. 2011. Precipitation variability over the forest to non-forest

transition in southwestern Amazonia. Journal of Climate 24: 2368–2377.

Kolstad, C.D. 1996. Fundamental irreversibilities in stock externalities. Journal of Public Economics 60(2):

221-233.

Kovacs, Kit M., Christian Lydersen, James E. Overland and Sue E. Moore. 2010. Impacts of changing sea-

ice conditions on Arctic marine mammals. Biodiversity 41(1) 181-194.

Kriegler, E., J. Hall, H. Held, R. Dawson, and H. Schellnhuber. 2009. Imprecise Probability Assessment of

Tipping Points in the Climate System. Proceedings of the National Academy of Sciences 106(13): 5041-

5046.

Kumar, K. Krishna, B. Rajagopalan, and M. Cane. 1999. On the weakening relationship between the

Indian Monsoon and ENSO. Science 284: 2156-2159.

Kumar, K. Krishna, Balaji Rajagopalan, Martin Hoerling, Gary Bates, and Mark Cane. 2006. Unraveling the

Mystery of Indian Monsoon Failure During El Niño. Science 314(5796): 115-119.

Kurz, W.A., C.C. Dymond, G. Stinson, G.J. Rampley, E.T. Neilson, A.L. Carroll, T. Ebata, and L. Safranyik.

2008a. Mountain pine beetle and forest carbon feedback to climate change. Nature 452: 987-990.

Kurz, W.A., Stinson, G., Rampley, G.J., Dymond, C.C. and Neilson, E.T.: 2008b. Risk of natural

disturbances makes future contribution of Canada's forests to the global carbon cycle highly uncertain.

Proceedings of the National Academy of Sciences 105: 1551-1555.

Lapola, D.M., M.D. Oyama, and C.A. Nobra. 2009. Exploring the range of climate biome projections for

tropical South America: The role of CO2 fertilization and seasonality. Global Biogeochemical Cycles 23,

GB3003.

Latif, M., and N.S. Keenlyside. 2009. El Nin o/Southern Oscillation response to global warming.

60

Proceedings of the National Academy of Sciences 106(49): 20578–20583.

Latif, M., C. BÖNING, J. WILLEBRAND, A. BIASTOCH, J. DENGG, N. KEENLYSIDE, U. SCHWECKENDIEK, and

G. MADEC. 2006. Is the Thermohaline Circulation Changing? Journal of Climate 19: 4631-4637.

Latif, M., E. Roeckner, U. Mikolajewicz, and R. Voss. 2000. Tropical stabilization of the thermohaline

circulation in a greenhouse warming simulation. Journal of Climate 13: 1809–1813.

Lawrence, David M., Andrew G. Slater, Vladimir E. Romanovsky, and Dmitry J. Nicolsky. 2008a.

Sensitivity of a model projection of near-surface permafrost degradation to soil column depth and

representation of soil organic matter. Journal of Geophysical Research 113, F02011,

Lawrence, David M., Andrew G. Slater, Robert A. Tomas, Marika M. Holland, and Clara Deser. 2008b.

Accelerated Arctic land warming and permafrost degradation during rapid sea ice loss. Geophysical

Research Letters 35, L11506.

Lawrence, David M., and Andrew G. Slater. 2010. The contribution of snow condition trends to future

ground climate. Clim. Dyn. 34(7-8): 969-981.

Lawrence, David M., and Andrew G. Slater. 2005. A projection of severe near-surface permafrost

degradation during the 21st century. Geophysical Research Letters 32, L24401.

Lemoine, D., and C. Traeger. 2012. Tipping points and ambiguity in the economics of climate change.

National Bureau of Economic Research, Cambridge, MA, Working Paper 18230.

Lenton T., H. Held, E. Kriegler, J. Hall, W. Lucht, and S. Rahmstorf. 2008. Tipping Elements in the Earth’s

Climate System. Proceedings of the National Academy of Sciences 105(6): 1786-1793.

Lenton, T. 2009. “Tipping Points in the Earth System.” Online. Accessed on August 09, 2011.

http://researchpages.net/ESMG/people/tim-lenton/tipping-points/.

Lenton. Lenton, T. M., A. Footitt, and A. Dlugolecki. 2009. Major Tipping Points in the Earth’s Climate

System and Consequences for the Insurance Sector (WWF/Allianz).

Lenton, T. 2011. Early Warning of Climate Tipping Points. Nature Climate Change 1: 201-109.

Lenton, T., and J-C. Ciscar. 2012. Integrating tipping points into climate impact assessments. Climatic

61

Change.

Levermann, A., Bamber, J., Drijfhout, S., Ganopolski, A., Haeberli, W., Harris, N.R.P., Huss, M., Krüger, K.,

Lenton, T.M., Lindsay, R.W., Notz, D., Wadhams, P. and Weber, S. 2012. Potential climatic transitions

with profound impact on Europe: Review of the current state of six tipping elements of the climate

system. Climatic Change 110: 845-878.

Levermann, A., J. Schewe, V. Petoukhov, and H. Held. 2009. Basic mechanism for abrupt monsoon

transitions. Proceedings of the National Academy of Sciences 106: 20572-20577.

Levermann, A., A. Griesel, M. Hofmann, M. Montoya, and S. Rahmstorf. 2005. Dynamic sea level changes

following changes in the thermohaline circulation. Climate Dynamics 24: 347–354.

Li, W., R. Fu, and R.E. Dickinson. 2006. Rainfall and its seasonality over the Amazon in the 21st century as

assessed by the coupled models for the IPCC AR4. Journal of Geophysical Research 111: D02111.

Lindsey, R.W., and J. Zhang 2005. The Thinning of Arctic Sea Ice, 1988–2003: Have We Passed a Tipping

Point? Journal of Climate 18: 4879-4894.

Link, P.M., and R.S.J. Tol. 2004. Possible economic impacts of a shutdown of the thermohaline

circulation: an application of FUND. Portuguese Economic Journal 3(2):99-114.

Link, P.M., and R.S.J. Tol. 2011. Estimation of the economic impact of temperature changes induced by a

shutdown of the thermohaline circulation: an application of FUND." Climatic Change 104(2):287-304.

Lucht, W,, S. Schaphoff, T. Erbrecht, U. Heyder, W. Cramer .2006. Terrestrial vegetation redistribution

and carbon balance under climate change. Carbon Balance and Management 1:6.

Lucht, W., I.C. Prentice, R.B. Myneni, S. Sitch, P. Friedlingstein, W. Cramer, P. Bousquet, W. Buermann, and B. Smith. 2002. Climatic control of the high-latitude vegetationgreening trend and Pinatubo effect. Science 296:1687-1689.

Malhi, Y., L.E.O.C. Aragao, D. Galbraith, C. Huntingford, R. Fisher, P. Zelazowski, S. Sitch, C. MecSweeney,

and P. Meir. 2009. Exploring the likelihood and mechanism of a climate-change-induced dieback of the

Amazon rainforest. Proceedings of the National Academy of Sciences 106: 20610-20615.

Marchenko, S., V. Romanovsky, and G. Tipenko. 2008. Numerical modeling of spatial permafrost

62

dynamics in Alaska. Proceedings, Ninth International Conference on Permafrost. 2: 1125-1130.

Mastrandrea, M., and S.Schneider. 2001. Integrated assessment of abrupt climatic changes. Climate

Policy 1(4): 433–449.

May, R. 1977. Thresholds and Breakpoints in Ecosystems with a Multiplicity of Stable States. Nature 269:

471-477.

Meehl, G.A., J.M. Arblaster, and W.D. Collins. 2008. Effects of Black Carbon Aerosols on the Indian Monsoon. Journal of Climate 21: 2869-2882.

Meehl, G. A. et al. 2007. The WCRP CMIP3 multimodel dataset: A new era in climate change research.

Bull. Am. Meteorol. Soc. 88: 1383–1394.

Meza, F.J., J.W. Hansen, and D. Osgood. 2008. Economic Value of Seasonal Climate Forecasts for

Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research. Journal of

Applied Meteorology and Climatology 47: 1269-1286.

Mignot, Juliette, and Claude Frankignoul. 2005. The Variability of the Atlantic Meridional Overturning

Circulation, the North Atlantic Oscillation, and the El Niño–Southern Oscillation in the Bergen Climate

Model. Journal of Climate 18: 2361-2375.

Mikolajewicz, U., M. Gröger, E. Maier-Reimer, G. Schurgers, M. Vizcaíno, and A. Winguth. 2007. Long-

term effects of anthropogenic CO2 emissions simulated with a complex earth system model. Climate

Dynamics 28: 599-633.

Mitrovica, J.X., N. Gomez, and P.U. Clark. 2009. The Sea-Level Fingerprint of West Antarctic Collapse.

Science 323, 753.

Mitrovica, J.X., M.E. Tamislea, J.L. Davis, and G.A. Milne. 2001. Recent mass balance of polar ice sheets

inferred from patterns of sea-level change. Nature 409: 1026-1029.

Moon, T., I. Joughin, B. Smith, and I. Howat. 2012. 21st-Century Evolution of Greenland Outlet Glacier

Velocities. Science 336(6081): 576-578.

Nævdal E., and M. Oppenheimer M. 2007. The economics of the thermohaline circulation—a problem

with multiple thresholds of unknown locations. Resource and Energy Economics 29:262–283.

63

Naish, T., Powell, R., Levy, R., Wilson, G., Scherer, R., Talarico, F., Krissek, L., Niessen, F., Pompilio, M.,

Wilson, T., Carter, L., DeConto, R., Huybers, P., McKay, R., Pollard, D., Ross, J., Winter, D., Barrett, P.,

Browne, G., Cody, R., Cowan, E., Crampton, J., Dunbar, G., Dunbar, N., Florindo, F., Gebhardt, C.,

Graham, I., Hannah, M., Hansaraj, D., Harwood, D., Helling, D., Henrys, S., Hinnov, L., Kuhn, G., Kyle, P.,

Laufer, A., Maffioli, P., Magens, D., Mandernack, K., McIntosh, W., Millan, C., Morin, R., Ohneiser, C.,

Paulsen, T., Persico, D., Raine, I., Reed, J., Riesselman, C., Sagnotti, L., Schmitt, D., Sjunneskog, C., Strong,

P., Taviani, M., Vogel, S., Wilch, T. and Williams, T. 2009. Obliquity-paced Pliocene West Antarctic ice

sheet oscillations. Nature 458: 322-328.

Narita, Daiju, Richard S.J. Tol, and David Anthoff. 2010. Economic costs of extratropical storms under

climate change: an application of FUND. Journal of Environmental Planning and Management 53(3):

371-384.

Narita, D., R.S.J. Tol, and D. Anthoff. 2009. Damage costs of climate change through intensification of

tropical cyclone activities: an application of FUND. Climate Research 39: 87–97.

National Academy of Sciences. 2011. Climate Stabilization Targets: Emissions, Concentrations, and

Impacts over Decades to Millennia. Washington, DC: National Academies Press, Inc.

National Academy of Sciences. 2010. Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use. Washington, DC: National Academies Press.

National Aeronautics and Space Administration (NASA). 2003. The Arctic Perennial Sea Ice Could Be

Gone by End of the Century. http://www.nasa.gov/vision/earth/environment/Perrenial_Sea_Ice.html.

National Research Council (2002). Abrupt Climate Change: Inevitable Surprises. National Academy Press,

Washington, DC.

Nicholls, R.J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel and R.S.J. Tol. 2011.

Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the twenty-first century. Phil. Trans.

R. Soc. A 369(1934): 161-181.

Nicholls, RJ, RSJ Tol, and AT Vafeidis. 2008. Global estimates of the impact of a collapse of the West

Antarctic ice sheet: an application of FUND. Climatic Change 91:171–191.

National Oceanic and Atmospheric Administration (NOAA). Future of Arctic Climate and Global Impacts.

64

http://www.arctic.noaa.gov/future/heat.html .

Nobre, C., and L. Borma. 2009. ‘Tipping points’ for the Amazon forest. Current Opinion in Environmental

Sustainability 1(1): 28–36.

Nordhaus, W.D. 2010. Economic aspects of global warming in a post-Copenhagen environment. Proc

Natl Acad Sci 107: 11721-11726.

Nordhaus, W. 2010. The Economics of Hurricanes and Implications of Global Warming. Climate Change

Economics 1(1): 1-20.

Nordhaus 2008. A Question of Balance: Weighing the Options on Global Warming Policies. New Haven,

CT: Yale University Press.

Nordhaus, W., and J. Boyer. 2000. Warming the world: economic models of global warming. Cambridge,

MA: The MIT Press.

Nordhaus, W. 1994a. Expert opinion on climatic change. American Scientist 82: 45–45.

Nordhaus, W. 1994b. Managing the Global Commons: the Economics of Climate Change. Cambridge,

MA: The MIT Press.

Overpeck, J., and J. Cole. 2006. Abrupt Change in Earth’s Climate System. The Annual Review of

Environment and Resources 31: 1-31.

Pardaens, A.K., J.M. Gregory, and J.A. Lowe. 2010. A model study of factors influencing projected

changes in regional sea level over the twenty-first century. Clim Dyn 36:2015–2033.

Patricola, C.M., and K.H. Cook. 2008 Atmosphere/vegetation feedbacks: A mechanism for abrupt climate

change over Northern Africa. Journal of Geophysical Research – Atmospheres 113, D18102.

Patz, J.A., D. Campbell-Lendrum, T. Holloway, and J.A. Foley. 2005. Impact of regional climate change on

human health. Nature 438: 310-317.

Perrings, C. 2003. The Economics of Abrupt Climate Change. Philosophical Transactions of the Royal

Society 361: 2043-2059.

65

Petoukhov, V., and V. Semenov. 2010. A link between reduced Barents-Kara sea ice and cold winter

extremes over northern continents. Journal of Geophysical Research 115, D21111.

Pfeffer, W., J. Harper, and S. O’Neel. 2008. Kinematic constraints on glacier contributions to 21st-century

sea-level rise. Science 321: 1340–1343.

Pielke, R. 2008. Climate predictions and observations. Nature Geoscience 1: 206.

Pielke, Jr, RA, R Klein, and D Sarewitz. 2001. Turning the big knob: An evaluation of the use of energy

policy to modulate future climate impacts. Energy and Environment 11(3): 255–275.

Pindyck, R.S. 2012. The Climate Policy Dilemma. NBER Working Paper No. w18205.

Pindyck, Robert S., and Neng Wang. 2012. The Economic and Policy Consequences of Catastrophes.

NBER Working Paper No. 15373, revised March.

Pindyck, R.S. 2007. Uncertainty in Environmental Economics. Review of Environmental Economics and

Policy 1(1): 45-65.

Pindyck, R.S. 2000. Irreversibilities and the Timing of Environmental Policy. Resource and Energy

Economics 22: 233-259.

Pollard, D., and R.M. DeConto. 2009. Modelling West Antarctic ice sheet growth and collapse through

the past five million years. Nature 458: 329-332.

Posner, R. 2004. Catastrophe: Risk and Response. New York: Oxford University Press.

Price, Stephen F., Antony J. Payne, Ian M. Howat, and Benjamin E. Smith. 2011. Committed sea-level rise

for the next century from Greenland ice sheet dynamics during the past decade. Proceedings of the

National Academy of Sciences 108(22): 8978-8983.

Rahmstorf, S., Cazenave, A., Church, J., Hansen, J., Keeling, R., Parker, D. & Somerville, R. 2007. Recent

climate observations compared to projections. Science 316: 709.

Ramanathan, V., Chung, C., Kim, D., Bettge, T., Buja, L., Kiehl, J.T., Washington, W.M., Fu, Q., Sikka, D.R.

and Wild, M. 2005. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle.

66

Proceedings of the National Academy of Sciences 102: 5326-5333.

Ramesh Kumar, M.R., R. Krishnan, S. Sankar, A.S. Unnikrishnan, D.S. Pai. 2009. Increasing Trend of

“Break-Monsoon” Conditions Over India—Role of Ocean–Atmosphere Processes in the Indian Ocean

Geoscience and Remote Sensing Letters, IEEE 6(2): 332-336.

Rammig, Anja, Tim Jupp, Kirsten Thonicke, Britta Tietjen, Jens Heinke, Sebastian Ostberg, Wolfgang

Lucht, Wolfgang Cramer, and Peter Cox. 2010. Estimating the risk of Amazonian forest dieback. New

Phytologist 187: 694–706.

Rockstrom, Johan, Will Steffen, Kevin Noone, Åsa Persson, F. Stuart Chapin, III, Eric F. Lambin, Timothy

M. Lenton, Marten Scheffer, Carl Folke, Hans Joachim Schellnhuber, Björn Nykvist, Cynthia A. de Wit,

Terry Hughes, Sander van der Leeuw, Henning Rodhe, Sverker Sörlin, Peter K. Snyder, Robert Costanza,

Uno Svedin, Malin Falkenmark, Louise Karlberg, Robert W. Corell, Victoria J. Fabry, James Hansen, Brian

Walker, Diana Liverman, Katherine Richardson, Paul Crutzen & Jonathan A. Foley. 2009. A Safe

Operating Space for Humanity. Nature 461: 472-475.

Roemmich, Dean, and John McGowan. 1995. Climatic Warming and the Decline of Zooplankton in the

California Current. Science 267(5202): 1324-1326.

Sagarin, Raphael D., James P. Barry, Sarah E. Gilman, and Charles H. Baxter. 1999. Climate Related-

Change in an Intertidal Community over Short and Long Time Scales. Ecological Monographs 69:465–

490.

Saito, K., M. Kimoto, T. Zhang, K. Takata, and S. Emori. 2007. Evaluating a high-resolution climate model:

simulated hydrothermal regimes in frozen ground regions and their change under the global warming

scenario. Journal of Geophysical Research 112, F02S11.

Schaefer, K., T. Zhang, L. Bruhwiler, and A.P. Barrett. 2011 Amount and timing of permafrost carbon

release in response to climate warming. Tellus 63B: 165–180

Schaphoff, S., W. Lucht, D. Gerten, S. Sitch, W. Cramer, and I.C. Prentice. 2006. Terrestrial biosphere

carbon storage under alternative climate projections. Climatic Change 74: 97-122.

Schlesinger, M., H. Kheshgi, J. Smith, F. de la Chesnaye, J. Reilly, T. Wilson, and C. Kolstad (2007).

Human-Induced Climate Change: An Interdisciplinary Assessment. New York: Cambridge University

67

Press.

Schmittner, A., and Stocker, T.F. 1999. The stability of the thermohaline circulation in global warming

experiments. Journal of Climate 12(4):1117-1133.

Schneider, S. 2003. Abrupt Non-Linear Climate Change, Irreversibility and Surprise. Workshop on the

Benefits of Climate Policy: Improving Information for Policy Makers. Organisation of Economic

Cooperation and Development. ENV/EPOC/GPS(2003)13/FINAL.

Scholze, M., W. Knorr, N.W Arnell, and I.C. Prentice. 2006. A climate-change risk analysis for world

ecosystems. Proceedings of the National Academy of Science 103(35): 13116-13120.

Screen, J., and I. Simmonds. 2012. Declining summer snowfall in the Arctic: causes, impacts and

feedbacks. Climate Dynamics 38(11-12): 2243-2256.

Screen, J., and I. Simmonds. 2010. The central role of diminishing sea ice in recent Arctic temperature

amplification. Nature 464: 1334-1337.

Seidov, D., R.J. Stouffer, and B.J. Haupt. 2005. Is there a simple bipolar ocean seesaw? Global Planet.

Change 49: 19-27.

Serreze, Mark C., and Roger G. Barry 2011. Processes and impacts of Arctic amplification: A research

synthesis. Global and Planetary Change 77: 85–96.

Sheffer, M., S. Carpenter, J. Foley, C. Folkes, and B. Walker. 2001. Catastrophic Shifts in Ecosystems.

Nature 413: 591-596.

Shindell, D. 2007. Estimating the Potential for Twenty-First Century Sudden Climate Change.

Philosophical Transactions: Mathematical, Physical and Engineering Sciences 365, 1860: 2675-2694.

Shindell, D.T., D. Rind, and P. Lonergan. 1998. Increased polar stratospheric ozone losses and delayed

eventual recovery owing to increasing greenhouse-gas concentrations. Nature 392: 589-592.

Solow, A.R., R.M. Adams, K.J. Bryant, D.M. Legler, J.J. O’Brien, B.A. McCarl, and W.I. Nayda. 1998. The

Value of Improved ENSO Prediction to U.S. Agriculture. Climatic Change 39: 47-60.

Sohngen, B., R.J. Alig, and B. Solberg. 2010. The Forest Sector, Climate Change, and the Global Carbon

68

Cycle—Environmental and Economic Implications. In Alig, R.J., tech. coord., Economic modeling of

effects of climate change on the forest sector and mitigation options: a compendium of briefing papers.

Gen. Tech. Rep. PNW-GTR-833. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station, pp. 37-66.

Stendel, M., and J.H. Christensen. 2002. Impact of global warming on permafrost conditions in a coupled

GCM. Geophysical Research Letters 29(13): 1632.

Stewart, E.J., S.E.L. Howell, D. Draper, J. Yackel and A. Tivy. 2007. Sea Ice in Canada's Arctic: Implications

for Cruise Tourism. Arctic 60(4): 370-380.

Stouffer, R.J., J. Yin, J.M. GREGORY, K.W. DIXON, M.J. SPELMAN, W. HURLIN, A.J. WEAVER, M. EBY, G.M.

FLATO, H. HASUMI, A. HU, J.H. JUNGCLAUS, I.V. KAMENKOVICH, A. LEVERMANN, M. MONTOYA, S.

MURAKAMI, S. NAWRATH, A. OKA, W.R. PELTIER, D.Y. ROBITAILLE, A. SOKOLOV, G. VETTORETTI, and S.L.

WEBER. 2006. Investigating the causes of the response of the thermohaline circulation to past and

future climate changes. Journal of Climate 19: 1365-1387.

Strey, S.T., W. Chapman, and J. Walsh (2009): Effects Of An Extreme Arctic Sea Ice Minimum On the

Northern Hemisphere Atmosphere During Late Autumn and Early Winter:, Eos Trans. Eos Trans. AGU,

90(52), Fall Meet. Suppl., Abstract C41A-0421.

Streove, Julienne C., Mark C. Serreze, Marika M. Holland, Jennifer E. Kay, James Malanik, and Andrew P.

Barrett. The Arctic’s rapidly shrinking sea ice cover: a research synthesis. Climatic Change 110: 1005-

1027.

Thorpe, R. B., J. M. Gregory, T. C. Johns, R. A. Wood, and J. F. B. Mitchell. 2001. Mechanisms determining

the Atlantic thermohaline circulation response to greenhouse gas forcing in a non-adjusted coupled

climate model. Journal of Climate 14: 3102–3116.

Timmermann, A., Oberhuber, J., Bacher, A., Esch, M., Latif, M. and Roeckner, E. 1999. Increased El Nino

frequency in a climate model forced by future greenhouse warming. Nature 398: 694-697.

Tol, R. 2009. An analysis of mitigation as a response to climate change. Copenhagen Consensus on

Climate. Discussion Paper.

Tol, R. 2002a. Estimates of the damage costs of climate change. Part I: benchmark estimates.

69

Environmental and Resource Economics 21: 47-73.

Tol, R. 2002b. Estimates of the damage costs of climate change. Part II: dynamic estimates.

Environmental and Resource Economics 21: 135-160.

Tsur, Y., and A. Zemel. 1996. Accounting for global warming risks: Resource management under event

uncertainty. Journal of Economic Dynamics and Control 20(6-7): 1289–1305.

Turner, A.G., and H. Annamalai. 2012. Climate change and the South Asian summer monsoon. Nature 2:

587-595.

Turner, A.G., and A. Hannachi. 2010. Is there regime behavior in monsoon convection in the late 20th

century? Geophysical Research Letters 37, L16706.

Turner, A.G., and J.M. Slingo. 2009. Subseasonal extremes of precipitation and active-break cycles of the

Indian summer monsoon in a climate-change scenario. Q. J. R. Meteorol. Soc. 135: 549–567.

United Nations Framework Convention on Climate Change (UNFCCC). 2009. Copenhagen Accord.

http://unfccc.int/resource/docs/2009/cop15/eng/l07.pdf.

United States Climate Change Science Program (USCCSP). 2008. Abrupt Climate Change. Synthesis and

Assessment Product 3.4.

U.S. Interagency Working Group on Social Cost of Carbon. 2010. Social Cost of Carbon for Regulatory

Impact Analysis under Executive Order 12866, http://www.whitehouse.gov/sites/default/

files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf.

Vellinga, M., and R.A. Wood. 2008. Impacts of thermohaline circulation shutdown in the twenty-first

century. Climatic Change 91: 43–63.

Vellinga, M., and R.A. Wood. 2002. Global climatic impacts of a collapse of the Atlantic thermohaline

circulation. Climatic Change 54(3): 251–267.

Wang and Overland 2009. A sea ice free summer Arctic within 30 years? GEOPHYSICAL RESEARCH

LETTERS, VOL. 36, L07502 http://mrc.org/pdf/WANG-OVERLAND-

ARCTIC%20SEA%20ICE%20ESTIMATE.pdf .

70

Weitzman, Martin. 2011. Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change.

Review of Environmental Economics and Policy 5(2): 275-292.

Weitzman, Martin. 2009. On Modeling and Interpreting the Economics of Catastrophic Climate Change.

Review of Economics and Statistics 91(1): 1-19.

White A., M.G.R. Cannell, and A.D. Friend. 1999. Climate Change Impacts on Ecosystems and the

Terrestrial Carbon Sink: a New Assessment. Global Environmental Change 9: S21-S30.

Williams, P. 2009. Rapid Climate Change: An Overview for Economists. International Journal of Green

Economics 3(1): 63-76.

Wilson, Katherina J., John Falkingham, Humfrey Melling, and Roger A. de Abreu. 2004. “Shipping in the

Canadian Arctic: Other Possible Climate Change Scenarios.” International Geoscience and Remote

Sensing Symposium, 2004. IGARSS ’04. Proceedings. IEEE International, vol. 3, pp. 1853–56.

World Meteorological Organization (WMO). 2006. Summary statement on tropical cyclones and climate

change. World Meteorological Organization, Geneva.

www.wmo.int/pages/prog/arep/tmrp/documents/iwtc_summary.pdf.

Yeh, S.-W., Kug, J.-S., Dewitte, B., Kwon, M.-H., Kirtman, B.P. and Jin, F.-F. 2009. El Nino in a changing

climate. Nature 461: 511-514.

Yohe, G. 1996. Exercises in hedging against extreme consequences of global change and the expected

value of information. Global Environmental Change 6(2): 87–101.

Zeng, N., R.E. Dickinson, and X. Zeng. 1996. Climatic impact of Amazon deforestation—a mechanistic

study. Journal of Climate 9: 859-883.

Zhang, D., R. Msadek, M.J. McPhaden, and T. Delworth. 2011. Multidecadal variability of the North Brazil

Current and its connection to the Atlantic meridional overturning circulation. Journal of Geophysical

Research 116, C04012.

Zhang, Xian Gdong. 2010. Sensitivity of arctic summer sea ice coverage to global warming forcing:

towards reducing uncertainty in arctic climate change projections. Tellus A 62(3): 220–227.

Zhang, Y., W.J. Chen, and D.W. Riseborough. 2008a. Transient projections of permafrost distribution in

71

Canada during the 21st century under scenarios of climate change. Global Planetary Change 60(3-4):

443-456.

Zhang, Y., W.J. Chen, and D.W. Riseborough. 2008b. Disequilibrium response of permafrost thaw to

climate warming in Canada over 1850–2100. Geophysical Research Letters 35.

Zickfeld, K., A. Levermann, M.G. Morgan, T. Kuhlbrodt, S. Rahmstorf, and D.W. Keith. 2007. Expert

Judgments on the Response of the Atlantic Meridional Overturning Circulation to Climate Change.

Climatic Change 82: 235-265.

Zickfeld, K., B. Knopf, V. Petoukhov, and H.J. Schellnhuber. 2005. Is the Indian summer monsoon stable

against global change? Geophysical Research Letters 32, L15707.

Zimov, Sergey. 2009. Implications of Ancient Ice. Science 323(5915): 714-715.

72

Table 1. Integrated Assessment Model Studies of Potential Climate “Catastrophes” Study Model Catastrophic event

analyzed Way catastrophe is represented Way damage function accounts for

catastrophic impacts Nordhaus (1994b); Nordhaus and Boyer (2000)

DICE Generic catastrophe

Based on expert elicitation

Yohe (1996) DICE augmented Generic catastrophe

discrete (two state) probability distribution that allows for the small probability of catastrophe

Carbon emissions result in large damages – e.g., loss of 12.5% of GDP for an average global annual temperature anomaly of 2.5 oC compared to 1.6% at 3 oC.

Gjerde et al. (1999)

regionalized IAM in which a social planner maximizes an additively separable intertemporal welfare function

Generic catastrophe

probability of event occurring is calibrated to the expert elicitation of Nordhaus (1994a)

piecewise utility function where welfare loss is instantaneous and permanent by a fixed amount if event occurs

Keller et al. (2000)

DICE augmented THC shutdown Assume THC would collapse after passing atmospheric carbon, based on the work of Stocker and Schmittner (1997)

• ad-hoc estimate for the welfare loss associated with a shutdown

• implicit assumption that after the threshold, welfare loss will be instantaneous, permanent

Mastrandrea and Schneider (2001)

DICE augmented THC weakening or shutdown

allow THC shutdown threshold to be a function of both the carbon stock and the rate at which the stock is increasing.

• allow welfare impacts of THC changes to be endogenous

• shutdown results in 1% - 25% global GDP loss above baseline.

• no judgement is made about the likelihood of cases studied

Link and Tol (2004)

FUND augmented THC shutdown represent THC shutdown by adjusting regional temperature anomalies using Ranhmstorf and Ganopolski (1999).

Temperature changes fed into FUND damage function to determine welfare loss.

Nicholls et al. (2008)

FUND augmented WAIS collapse • ad-hoc melting scenarios, ranging from 0.5 to 5 m sea level rise by 2130

• Temperature changes fed into FUND damage function to determine welfare loss

• Damages are a function of rate of sea level rise and its interactions with adaptation measures

73

Hope (2011) PAGE 2009 Generic catastrophe

• probability of event occurring is zero until a given threshold is reached after which the probability begins to rise

• probabilities chosen in a fairly ad-hoc manner

• permanent reduction in welfare, but it’s not instantaneous.

• transition period is considered uncertain, ranging 20-200 yrs

• range of welfare impacts chosen fairly ad-hoc. Lower end of EU losses based on SLR damages in Anthoff et al. (2006). Damages in other regions are based on their coastline length relative to EU.

Lemoine and Traeger (2012)

DICE modified to consider parametric uncertainty in the temperature threshold and stochastic uncertainty in the temperature dynamics

two types of tipping points:

• 1) increases effect of emissions on temp (e.g., rapid retreat of land ice sheets, releases from CH4 deposits);

• 2) increases atmospheric lifetime of CO2 (e.g., weakening of carbon sinks)

• Tipping point 1 represented by increasing climate sensitivity from 3 deg to 4, 5, or 6.

• Tipping point 2 represented by reducing CO2 decay rate, i.e., decreasing the transfer of CO2 out of the atmosphere by 25%, 50%, or 75%.

• assume 2040 trigger (central point of the distribution) under no policy

• passing threshold results in instantaneous permanent shock

Does not force modeled catastrophes as a direct shock to welfare, but assumptions regarding the magnitude of the effects are ad-hoc

Link and Tol (2011)

FUND augmented THC shutdown • temperature anomalies from shutdown based on OAGCM experiments

• impact of the shutdown phased in linearly 2070-2100

Temperature changes fed into FUND damage function to determine welfare loss.

Ceronsky et al (2011)

FUND augmented • THC weakening • large scale CH4

release from deep ocean

• assume event would occur with certainty • represent THC shutdown with regional

temperature anomalies from Ranhmstorf & Ganopolski (1999).

• Represent CH4 release with instantaneous fixed increase in CH4 emissions in 2050. Timing, level of the shift based on judgement. Sensitivity analysis around level

Temperature changes fed into FUND damage function to determine welfare loss.

74

Table 2. Description and Key Characteristics of Potential Climate “Catastrophes”

Potential "Catastrophe"

Description/Cause Lenton et al. (2008)

Trigger level of global warming

Transition timescale to

new state

1 Melting of Arctic summer sea-ice Higher atmospheric temperatures and numerous feedback effects (e.g., reduced Arctic summer snowfall) cause the Arctic sea-ice to melt completely by late summer.

+0.5-2 C ~10 yr

2 Collapse of Greenland ice sheet (GIS) Higher atmospheric temperatures and numerous feedback effects can commit to a retreat and complete melting of the ice sheet.

+1-2 C >300 yr

3 Collapse of West Antarctic ice sheet (WAIS)

Higher atmospheric temperatures and numerous feedback effects can commit to a retreat and complete melting of the ice sheet. +3-5 C >300 yr

4 Change in amplitude/frequency/variability of ENSO

Many of the mechanisms and physical feedbacks that control the characteristics of ENSO are expected to be affected by rising GHG emissions – e.g., a weakening of tropical Pacific easterly trade winds, changes in surface ocean temperatures and ocean temperature gradients near the equator. Strong transient ENSO responses and shifts to new ENSO state are possible, but models are inconsistent in magnitude and direction of change.

+3-6 C ~100 yr

5 Dieback of Amazon rainforest

Climate change induced dieback of the Amazon rainforest is generally thought to be due to widespread reductions in precipitation and lengthening of the dry season, primarily due to more persistent El Nino conditions. However, land use change may also play a significant role in tipping point behavior.

+3-4 C ~50 yr

6 Dieback of Boreal forest

Boreal forests (contained in northern regions of Russia, Scandinavia, Canada and Alaska) are projected to be vulnerable to rising temperatures and other impacts of climate change, with increased water stress and increased peak summer heat stress leading the trees to be more vulnerable to pests, disease, mortality, and fires, along with decreased reproduction rates.

+3-5 C ~50 yr

7 Weakening/Shutdown of Atlantic Thermohaline Circulation (THC)

The THC, an ocean water circulation pattern responsible for a large fraction of northward heat transport of the Atlantic Ocean, may weaken or shutdown due to changes in water density and pressure gradients at high latitudes, especially the addition of freshwater into the North Atlantic from higher precipitation and ice melt and the warming of surface waters from higher atmospheric temperatures.

+3-5 C ~100 yr

75

8 Collapse of West African monsoon (WAM) /Greening of the Sahel

The West African monsoon (WAM) summer rains are affected by sea surface temperatures, so any changes in the Atlantic THC could also have implications for the WAM, and thus the Sahel region. A weakening of the THC could cause a shift in the WAM, with ramifications for precipitation in the Sahel, but there is significant uncertainty about the direction of physical impacts,

+3-5 C ~10 yr

9 Collapse/Volatile Indian Summer Monsoon (ISM)

The ISM refers to the rainy season that supplies the Indian sub-continent with about 80% of its annual rainfall. Increasing CO2 and temperatures, together with brown haze, affect the thermal contrast between land and sea needed to build up a monsoon. The result may be increased, chaotic volatility in monsoon strength, location, or even a potential collapse. Changes in ENSO may also affect ISM volatility.

N/A ~1 yr

10 Retreat of Tundra

Rising temperatures could allow the northern boundary of the boreal forest to encroach on tundra (a biome where the tree growth is hindered by low temperatures and short growing seasons), leading to amplified warming as the trees obscure the snow.

- ~100 yr

11 Permafrost thaw in Siberia Rising temperatures can cause vast thawing of permafrost (carbon rich ground that is at or below 0C for at least two consecutive years), leading to significant amplified warming effects.

- <100 yr

12 Weakening/Shutdown of Antarctic Bottom Water (AABW) formation

Similar to the THC, the AABW may weaken or even collapse due to freshening of the water in the Southern Ocean.

unclear*

13 Massive release of marine methane hydrates

Rising ocean temperatures (and eventually sediment layer temperatures) could trigger a massive release of methane from sea floors, leading to significant amplified warming effects.

unclear >1,000 yr

14 Ocean anoxia

Ocean anoxia occurs when the ocean is completely deleted of oxygen, causing mass extinctions. Anoxia is exacerbated by sustained phosphorus input to the ocean (e.g., from human agricultural fertilizer application), and higher temperatures are expected to further accelerate weathering processes which release phosphorus.

unclear ~10,000 yr

15 climate-induced Arctic ozone hole A climate change-induced ozone hole could form as higher temperatures cool the stratosphere that supports formation of ice clouds, which in turn provide a catalyst for stratospheric ozone destruction.

unclear <1 yr

*Either the trigger level of warming is not established or global warming is not the only or the dominant forcing.

76

Table 3. Recent Ranking or Categorization of Potential Climate “Catastrophes”

Potential "Catastrophe"

“Policy Relevant” Tipping Point

(Lenton et al. 2008)

Tipping Points “Of Greatest Concern”

(Allison et al. 2009)*

Relative likelihood of occurring

(Lenton et al. 2011)** Relative impact

(Lenton et al. 2011)** 1 Melting of Arctic summer sea-ice X High Low 2 Collapse of Greenland ice sheet (GIS) X X Med-High Med-High

3 Collapse of West Antarctic ice sheet (WAIS)

X X Med High

4 Change in amplitude/variability of ENSO X Low Med-High

5 Dieback of Amazon rainforest X X Med Med

6 Dieback of Boreal forest X Low Med-Low

7 Weakening/Shutdown of Atlantic Thermohaline Circulation (THC)

X Low Med

8 Collapse of West African monsoon (WAM) /Greening of the Sahel

X X Med-Low High

9 Collapse/Volatile Indian Summer Monsoon (ISM)

X X (not considered) (not considered)

10 Retreat of Tundra (not considered) (not considered) 11 Permafrost thaw in Siberia (not considered) (not considered)

12 Weakening/Shutdown of Antarctic Bottom Water (AABW) formation

(not considered) (not considered)

13 Massive release of marine methane hydrates

(not considered) (not considered)

14 Ocean anoxia (not considered) (not considered)

15 climate-induced Arctic ozone hole (not considered) (not considered) *Allison et al. (2009) define tipping points of “greatest concern” as those that are “the nearest (least avoidable) and those that have the largest negative impacts.” ** Lenton’s (2011) assessment of relative likelihoods and impacts are assessed on a five-point scale: low, low-medium, medium, medium-high and high. His likelihood rankings are based on his reviews of the literature and expert elicitation (Kriegler et al 2009). Impacts are based on limited research (Lenton et al. 2009) and subjective judgment, and are relative to the one system (THC) with multiple impacts studies. Impacts are considered on the full ‘ethical time horizon’ of 1,000 years, assuming minimal discounting of impacts on future generations.

77

Table 4. Scope for Near Term IAM Modeling Improvements?

Potential "Catastrophe" Likelihood of significant physical impacts occurring this century*

Scientific consensus in how physical impacts

will unfold?**

Physical endpoints for which (at least 21st C) projections are available

1 Melting of Arctic summer sea-ice High, changes already observed More September sea ice extent, regional winter temperature and precipitation impacts

2 Collapse of Greenland ice sheet (GIS) Medium-High More Sea level rise

3 Collapse of West Antarctic ice sheet (WAIS) Medium-High More Sea level rise

4 Change in amplitude and/or variability of ENSO

Medium Less change in ENSO amplitude, frequency, and variability ??

5 Dieback of Amazon rainforest Medium-High Less Change in tree cover, vegetation and soil carbon, precipitation, amplified regional warming; pdfs for change in vegetation carbon storage (kg C m-2) by region

6 Dieback of Boreal forest Medium More

7 Weakening/Shutdown of Atlantic Thermohaline Circulation (THC)

Medium-Low Less Regional change in temperature, precipitation, sea level from hypothetical instantaneous hosing experiment

8 Collapse of West African monsoon (WAM) /Greening of the Sahel

Medium-Low Less

9 Collapse/Volatile Indian Summer Monsoon (ISM)

High, changes already observed Less?

10 Retreat of Tundra High, changes already observed More

11 Permafrost thaw High, changes already observed More Change in active layer depth and extent of permafrost area, accompanying atmospheric carbon flux

12 Weakening/Shutdown of Antarctic Bottom Water (AABW) formation

Needs more study Less

13 Massive release of marine methane hydrates Low More?

14 Ocean anoxia Low in deep ocean. Coastal areas

need more study More?

15 climate-induced Arctic ozone hole Needs more study More?

*This does not necessarily mean a tipping point is transgressed this century. **By consensus we mean a general understanding of how earth systems will respond (e.g., which physical endpoints will be affected and the direction of impact on these endpoints) rather than scientific agreement on the detailed modeling and projections of physical impacts.

78

Table 5. Primary Physical Impacts Leading to Economic Consequences of Climate “Catastrophes”*

Potential "Catastrophe" Changes in Temperature Sea Level

Rise Changes in

Precipitation

Shifts in frequency/magnitude of

extreme weather events**

Other – e.g., impacts on ecosystems/species/bidiversity?

Direct From additional GHG feedback

1 Melting of Arctic summer sea-ice

X (hemispheric) X (CO2 & CH4) X X X

2 Collapse of Greenland ice sheet (GIS)

X (local) X (CO2 & CH4) X (global) X

3 Collapse of West Antarctic ice sheet (WAIS)

X (local) X (CO2 & CH4) X (global) X

4 Change in amplitude/variability of ENSO

X (regional) X (CO2) X (regional) X X X

5 Dieback of Amazon rainforest

X (regional) X (CO2) X X X

6 Dieback of Boreal forest

X (local) X (CO2) X? X X

7

Weakening/Shutdown of Atlantic Thermohaline Circulation (THC)

X (hemispheric) X (CO2) X (regional) X X X

8

Collapse of West African monsoon (WAM) /Greening of the Sahel

X (regional) X X X

9 Collapse/Volatile Indian Summer Monsoon (ISM)

X (local summer)

X X

10 Retreat of Tundra X (regional?) X

11 Permafrost thaw in X (regional?) X X

79

Siberia

12

Weakening/Shutdown of Antarctic Bottom Water (AABW) formation

X ? ? ? ? ?

13 Massive release of marine methane hydrates

X (CH4) X?

14 Ocean anoxia ? X

15 climate-induced Arctic ozone hole

X (regional) X

* This assessment is based on Lenton and Ciscar (forthcoming) and our review of scientific literature (see Appendix). An “X” indicates the physical impact is expected as a result of the “climate catastrophe” occurring. For some columns, additional information is provided in parentheses about the expected extent of the impact. Shaded boxes indicate physical impacts that have received the most attention by scientists – either because they are expected to be the largest/most significant sources of economic damage associated with the “climate catastrophe” s or because more is known to date about how these physical endpoints will evolve. **e.g., drought, floods, fire, hurricanes, other storms.